基于超阈法的海洋工程极端环境条件重现期值计算理论与方法研究
|
摘要
目前世界经济进入资源环境瓶颈期，陆地资源和空间所承载的压力越来越大，世界各国纷纷将开发资源丰富的海洋纳入国家发展战略。但是海洋环境也正在逐渐变得恶劣，各种海洋灾害频发，对各种海洋工程装备和设施提出更高的要求。准确推算海洋极端环境要素出现概率和大小对海洋工程的安全性和经济性起到至关重要的作用。
在海洋工程结构设计中，海洋极端环境条件及其导致的环境荷载的确定是重要的基础环节之一。目前，各国技术规范中估计海洋环境设计参数基本都选取每年最大值进行统计，该方法严重浪费数据，从原始数据中只获取少量信息，需要相对长期资料才能保证估计值合理性；另外各国现行方法中主要采用单因素频率分析法估计海洋环境要素设计参数，由于该方法没有利用联合分布分析方法考虑多种海洋环境条件共同作用，各种海洋环境条件的影响都是独立分析后再简单叠加，可能导致设计参数偏大，从而增加工程的建设成本。本文主要从一维和多维两个方面对超阈模型进行研究，主要研究内容和创新点如下：
首先分析一维超阈法取样，主要提出超阈模型分析海洋环境参数中新的取样分组方法和解决选取最佳阈值的问题。分析分组的基本原则以及现有的分组方法的优缺点，把握分组中关键因素：区分大浪与小浪以及两个连续大浪位置，结合时间窗口分组和双阈值分组方法提出新的超阈模型分组方法；利用超阈模型中的GPD模型和PDS模型与传统的分组方法对比，可知本文分组方法可行。
目前在我国P-G (泊松-龚贝尔)复合分布模型使用较多，该模型在台风不明显海域使用时同样需要面临选阈的难题。本文通过分析P-G复合分布法取阈原理，结合变点统计理论，提出新的取阈方法。通过多组长期波浪数据比较验证，证明该取阈方法可行。将该取阈值结合P-G复合分布模型能用于台风浪后报数据进行分方向的极值统计。
针对利用多维联合概率分析多种环境条件的共同影响，本研究提出多维超阈模型及其随机模拟求解方法。类似一维超阈分布于极值分布关系，建立多维超阈分布模型，首次提出多维泊松复合超阈分布模型。该分布模型能考虑每年超阈大浪过程频次的影响，用多维超阈理论分布来拟合超阈样本，较其它多维分布拟合超阈样本更有理论依据。提出多维超阈分布共同阈值求解方案，但仍需改进以克服人为因素的影响。根据多维超阈分布定义，利用拒绝法建立多维超阈分布的随机模拟方法步骤，进一步结合多维复合分布原理，建立多维泊松复合超阈分布的随机数产生方法，为多维超阈模型的高维求解提供了可靠的随机模拟解法。
以南海遮浪水文观测站23年(1960-1982)波浪和风速同步实测资料为例，确定其共同阈值，用二维超阈模型计算其联合分布概率，结合基底剪力经验公式计算其多年重现期值。利用随机模拟方法求解该算例，随机模拟结果和解析解结果接近，证明本文随机模拟方法可行。随机模拟方法很容易类推至高维，为高维的超阈模型分析海洋工程多种极端环境条件问题提供可行方法。
At present the world economy has been met the bottleneck period of resources andenvironment, and limited resource and space on land are under enormous pressure.Many countries have taken developing the marine economy as the nationaldevelopment strategy in the future. But, when humanity is preparing to develop andutilizate marine resources on a large scale, our marine environments are becomingworsen. And many more severe oceanic disasters come more frequently than before.Undoubtedly, it puts towards a high request on design of offshore equipments. How tocorrectly predict the probabilities of their occurrence and strength of the extreme oceanenvironmental conditions is essential for safety and economics of ocean engineerings.
In the design of ocean engineerings, the accurate predictability of the returnperiod level of the extreme ocean environmental conditions is a very fatal part. Atpresent, sampling method of annual maximum series was used in these major countries.However the method makes a serious waste of the raw data, and can only get very littleinformation from the raw data. For ensuring the rationality and reliability of the result,the annual maximum series method needs the long-term observation data. Moreover,the univariate probability analysis method is commonly used, and a more objectiveanalyze method-the joint probability method is rarely used. In the univariateprobability analysis method, each environmental load is estimated independently andmakes a simply an addition of the all factors, which results in higher design parametersand construction cost. The paper studies mainly the POT (Peak Over Threshold) modelfrom in two sides: one-dimension and multi-dimension, and its main idea andinnovations are as follows:
The paper solve mainly those problems about choosing the threshold value andgrouping in analysing extreme ocean environmental conditions with POT. According toa literature search, all grouping methods are summarized and compared theiradvantages and disadvantages. Sticking to the points in grouping: to discriminate thebig wavs from all waves and to driving adjacent the big waves apart，the new method ofgrouping, what is usable in the POT model, is proposed. By contrasting the traditionalmethods, the method of grouping in the paper is feasible, and POT model GPD andPDS have exactly the same results. Using the new grouping method, POT model can beused in every sea area, not in the sea area affected by only typhoon easily, such as thecompound distribution model.
At present P-G compound distribution is used quite frequently in lots of projectsin our country. But in some seas, in which typhoon is barely to be seen, there is thepuzzle of determining the threshold value for P-G compound distribution yet. Byanalyzing the principle of determining the thresholding value of P-G compounddistribution, the new method of determining the threshold value is proposed in thepaper combining the change point thory. The new method has the theoreticalunderpinnings of the mathematical principle, so it is not affected by human' ssubjectivity. And the proposed method can determine the threshold by a scientificquantitative method too. By comparing the traditional methods with search groups oflong-term databases, The proposed method is proved to be viable. For hindcast data ofocean wave, P-G compound distribution can be used for the return wave height in everydirection with the method of determining the thresholding value, but the P-Ⅲ methodcan not do it.
The reseach propose multidimensional compound POT distribution model and itsMonte Carlo Simulation method. First, similar to the relationship between generalizedPareto distribution and generalized extreme value distribution, Multivariategeneralized Pareto distribution model is established. And the paper proposes firstly themultivariate Piossion-Pareto compound distribution. It include these influents of theoccurrences of such storms (or the big wave) in certain sea areas and the theoreticaldistribution of multivariate POT model. The method of deciding the joint threshold isproposed, but needs improved further for overcoming contrived factor. According tothe definition of multivariate generalized Pareto distribution, the Monte CarloSimulation method of multivariate generalized Pareto distribution is set up with therejection method. Combining the multivariate compound distribution, the Monte CarloSimulation method of the multivariate Pareto compound distribution is firstly proposed.Because the method is extended to higher dimensional problems easily, a practical wayis found for multidimensional problems.
Taking23years field synchronizing data of wave height and wind speed as anexample, the represent return periods of the design base shear of a oil platform areanalyzed. After deciding the joint threthod of wave height and wind speed, the returnperiod levels of the base shear are found combining bivariate POT model of logistictype. Parameters sensitivity analysis to the bivariate pareto distribution model are done,and it is showed that the model is very stable. By comparing the results of the MonteCarlo Simulation and analytic solution, a good agreement with deviation is demonstrated. So it can be knowed that the peoposed Monte Carlo method is entirelyfeasible. The Monte Carlo Simulation method can be extrended to higher dimensionalproblem.
在海洋工程结构设计中，海洋极端环境条件及其导致的环境荷载的确定是重要的基础环节之一。目前，各国技术规范中估计海洋环境设计参数基本都选取每年最大值进行统计，该方法严重浪费数据，从原始数据中只获取少量信息，需要相对长期资料才能保证估计值合理性；另外各国现行方法中主要采用单因素频率分析法估计海洋环境要素设计参数，由于该方法没有利用联合分布分析方法考虑多种海洋环境条件共同作用，各种海洋环境条件的影响都是独立分析后再简单叠加，可能导致设计参数偏大，从而增加工程的建设成本。本文主要从一维和多维两个方面对超阈模型进行研究，主要研究内容和创新点如下：
首先分析一维超阈法取样，主要提出超阈模型分析海洋环境参数中新的取样分组方法和解决选取最佳阈值的问题。分析分组的基本原则以及现有的分组方法的优缺点，把握分组中关键因素：区分大浪与小浪以及两个连续大浪位置，结合时间窗口分组和双阈值分组方法提出新的超阈模型分组方法；利用超阈模型中的GPD模型和PDS模型与传统的分组方法对比，可知本文分组方法可行。
目前在我国P-G (泊松-龚贝尔)复合分布模型使用较多，该模型在台风不明显海域使用时同样需要面临选阈的难题。本文通过分析P-G复合分布法取阈原理，结合变点统计理论，提出新的取阈方法。通过多组长期波浪数据比较验证，证明该取阈方法可行。将该取阈值结合P-G复合分布模型能用于台风浪后报数据进行分方向的极值统计。
针对利用多维联合概率分析多种环境条件的共同影响，本研究提出多维超阈模型及其随机模拟求解方法。类似一维超阈分布于极值分布关系，建立多维超阈分布模型，首次提出多维泊松复合超阈分布模型。该分布模型能考虑每年超阈大浪过程频次的影响，用多维超阈理论分布来拟合超阈样本，较其它多维分布拟合超阈样本更有理论依据。提出多维超阈分布共同阈值求解方案，但仍需改进以克服人为因素的影响。根据多维超阈分布定义，利用拒绝法建立多维超阈分布的随机模拟方法步骤，进一步结合多维复合分布原理，建立多维泊松复合超阈分布的随机数产生方法，为多维超阈模型的高维求解提供了可靠的随机模拟解法。
以南海遮浪水文观测站23年(1960-1982)波浪和风速同步实测资料为例，确定其共同阈值，用二维超阈模型计算其联合分布概率，结合基底剪力经验公式计算其多年重现期值。利用随机模拟方法求解该算例，随机模拟结果和解析解结果接近，证明本文随机模拟方法可行。随机模拟方法很容易类推至高维，为高维的超阈模型分析海洋工程多种极端环境条件问题提供可行方法。
At present the world economy has been met the bottleneck period of resources andenvironment, and limited resource and space on land are under enormous pressure.Many countries have taken developing the marine economy as the nationaldevelopment strategy in the future. But, when humanity is preparing to develop andutilizate marine resources on a large scale, our marine environments are becomingworsen. And many more severe oceanic disasters come more frequently than before.Undoubtedly, it puts towards a high request on design of offshore equipments. How tocorrectly predict the probabilities of their occurrence and strength of the extreme oceanenvironmental conditions is essential for safety and economics of ocean engineerings.
In the design of ocean engineerings, the accurate predictability of the returnperiod level of the extreme ocean environmental conditions is a very fatal part. Atpresent, sampling method of annual maximum series was used in these major countries.However the method makes a serious waste of the raw data, and can only get very littleinformation from the raw data. For ensuring the rationality and reliability of the result,the annual maximum series method needs the long-term observation data. Moreover,the univariate probability analysis method is commonly used, and a more objectiveanalyze method-the joint probability method is rarely used. In the univariateprobability analysis method, each environmental load is estimated independently andmakes a simply an addition of the all factors, which results in higher design parametersand construction cost. The paper studies mainly the POT (Peak Over Threshold) modelfrom in two sides: one-dimension and multi-dimension, and its main idea andinnovations are as follows:
The paper solve mainly those problems about choosing the threshold value andgrouping in analysing extreme ocean environmental conditions with POT. According toa literature search, all grouping methods are summarized and compared theiradvantages and disadvantages. Sticking to the points in grouping: to discriminate thebig wavs from all waves and to driving adjacent the big waves apart，the new method ofgrouping, what is usable in the POT model, is proposed. By contrasting the traditionalmethods, the method of grouping in the paper is feasible, and POT model GPD andPDS have exactly the same results. Using the new grouping method, POT model can beused in every sea area, not in the sea area affected by only typhoon easily, such as thecompound distribution model.
At present P-G compound distribution is used quite frequently in lots of projectsin our country. But in some seas, in which typhoon is barely to be seen, there is thepuzzle of determining the threshold value for P-G compound distribution yet. Byanalyzing the principle of determining the thresholding value of P-G compounddistribution, the new method of determining the threshold value is proposed in thepaper combining the change point thory. The new method has the theoreticalunderpinnings of the mathematical principle, so it is not affected by human' ssubjectivity. And the proposed method can determine the threshold by a scientificquantitative method too. By comparing the traditional methods with search groups oflong-term databases, The proposed method is proved to be viable. For hindcast data ofocean wave, P-G compound distribution can be used for the return wave height in everydirection with the method of determining the thresholding value, but the P-Ⅲ methodcan not do it.
The reseach propose multidimensional compound POT distribution model and itsMonte Carlo Simulation method. First, similar to the relationship between generalizedPareto distribution and generalized extreme value distribution, Multivariategeneralized Pareto distribution model is established. And the paper proposes firstly themultivariate Piossion-Pareto compound distribution. It include these influents of theoccurrences of such storms (or the big wave) in certain sea areas and the theoreticaldistribution of multivariate POT model. The method of deciding the joint threshold isproposed, but needs improved further for overcoming contrived factor. According tothe definition of multivariate generalized Pareto distribution, the Monte CarloSimulation method of multivariate generalized Pareto distribution is set up with therejection method. Combining the multivariate compound distribution, the Monte CarloSimulation method of the multivariate Pareto compound distribution is firstly proposed.Because the method is extended to higher dimensional problems easily, a practical wayis found for multidimensional problems.
Taking23years field synchronizing data of wave height and wind speed as anexample, the represent return periods of the design base shear of a oil platform areanalyzed. After deciding the joint threthod of wave height and wind speed, the returnperiod levels of the base shear are found combining bivariate POT model of logistictype. Parameters sensitivity analysis to the bivariate pareto distribution model are done,and it is showed that the model is very stable. By comparing the results of the MonteCarlo Simulation and analytic solution, a good agreement with deviation is demonstrated. So it can be knowed that the peoposed Monte Carlo method is entirelyfeasible. The Monte Carlo Simulation method can be extrended to higher dimensionalproblem.
引文
[1]狄乾斌韩增林.我国海洋资源开发综合效益的评价探讨[J].国土与自然资源研究,2003,(3):16-18.
[2]梁飞.海洋经济和海洋可持续发展理论方法及其应用研究[D].天津大学，2003
[3]储永萍，蒙少东.发达国家海洋经济发展战略及对中国的启示[J].湖南农业科学,2009，（8）：154~157.
[4] IPCC. Climate change2007: The scientifie basis. Cambridge university Press，Cambridge，2007.
[5] P.J.Webster,G.J.Holland,J.A.Curry,H.-R.Chang. Changes in Tropical CycloneNumber, Duration, and Intensity in a Warming Environment [J], Science,2005,309:1844-1846.
[6]林而达，许吟隆，蒋金荷等.气候变化国家评估报告(II):气候变化的影响与适应.气候变化研究进展，2006，2(2):5-56.
[7]国家海洋局，2003年中国海平面公报.
[8]国家海洋局，2009年中国海平面公报.
[9]中国科学院地学部．海平面上升对中国三角洲地区的影响及对策.北京：科学出版社，1994：1～12
[10]国家海洋局，2012年中国海平面公报。
[11]殷杰．中国沿海台风风暴潮灾害风险评估研究[D]．华东师范大学，2011
[12]杨桂山，施雅风．西北太平洋热带气旋频数的变化及与海表温度的相关研究[J].地理学报，1993，54(l):22-2
[13]施雅风.我国海岸带灾害的加剧发展及其防御方略[J].自然灾害学报，1994，3(2)：3～l.
[14]杨桂山．中国沿海风暴潮灾害的历史变化及未来趋向[J].自然灾害学报，2000，9(3):23-30.
[15]夏东兴，刘振夏等．渤海湾西岸海平面上升威胁的防治对策[J]．自然灾害学报，1993，2(1)：48~52
[16]朱季文，季子修等．海平面上升对长江三角洲及邻近地区的影响[J].地理科学，1995，1(2)：109~11.
[17]何洪，邹斌等.海平面上升对广东沿海风暴期灾害的可能影响[J].广东省水文总站，1995.
[18]季子修，施雅风.海平面上升、海岸带灾害与海岸防护问题[J].自然灾害学报，1996,5(2):56-64
[19]陆儒德.海洋-国家-海权[M].北京:海潮出版社,2000.135-180.
[20]李玉成.海洋工程技术进展与对发展我国海洋经济的思考[J].大连理工大学学报,2002,42(1):1-5.
[21]国家海洋局，2012中国海洋灾害公报,2012
[22]邓松，刘雪峰，游大伟等．广东省1991-2005年５种主要海洋灾害概况[J]．广东气象．2006，11(4):19-22．
[23]国家海洋局，2009年中国海洋灾害公报
[24]刘文通，楼顺里，宋珊，吴术礼.海上石油平台对周围风场的影响[J].青岛海洋大学学报，1990，20(3)：39-48.
[25]赵声振．对我国海上边际油田开发价值预期的反思．国际石油经济，2004，12（8）：50~60.
[26]刘伟.基于最大熵分布的海洋平台环境条件联合重现值推算[D].中国海洋大学，青岛，2011.
[27]黎志均，刘永锋．台风“伊布都“对珠海市桂山岛某湾东防波堤损坏原因分析[J]．中国港湾建设.2006，5：10-11．
[28]董胜,李奉利,孙瑞文.风暴增水随机分析的过阈法及其统计计算模式[J].青岛海洋大学学报,2000,30(3):542-548.
[29] Guedes Seares C． and T． Moan． On the uncertainties to the extremehydrodynamic loading of a cylindrical plie，reliability theory and its application[J]，Structural&Soil Mechanics，Mantiuns Nijhoft Pub．，Hague，l983．351-364．
[30] Haver S., Analysis of uncertainties related to the stochastic modelling of oceanwaves [J]．Report UR8009．NTH l980，183-186．
[31] Haver S., T. Moan．On some uncertainties related to the short-term stochasticmodelling of ocean waves [J]，Applied Ocean Research，5(1983),2:35-48．
[32] Liu Teh fu, wang Chao, Design wave criteria for offshore structures[J]．offshore Mechanics and Arctic Engineering,1986,1:404-409．
[33] Leese, M.N.. Use of censored data in the estimation of the Gumbel distributionparameters for annual maximum flood series [J], Water Resour. Res.,1973,9(6),1534-1542.
[34] Francés, F., J.D. Salas and D.C. Boes. Flood frequency analysis withsystematic and historical or paleoflood data based on the two-parameter generalextreme value models [J]. Water Resour. Res.,1994,30(6),1653-1664.
[35] YOU Zai-jin, YIN Bao-shu. Estimation of Extreme Coastal Wave HeightsFrom Time Series of Wave Data [J]. China Ocean Engineering,2006，20(2),225-241
[36]马逢时，刘德辅.复合极值分布理论及其应用.应用数学学报，1979,2(4),365-375.
[37]交通部第一航务工程勘察设计院.海港水文规范．JTJ213-98北京：人民交通出版社，1998.
[38] NERC. Flood Studies Report, Vol.1,1975. Natural Enviroment ResearchCouncil, London.
[39]史道济.实用极值统计方法[M].天津:天津科学技术出版社,2006
[40] Mazas, F., Hamm, L.. A multi-distribution approach to POT methods fordetermining extreme wave heights [J]. Coastal Engineering,2011，58,385–394.
[41]王超,刘德辅.设计波浪选取中的不确定性分析[J].海洋学报，1991，13(4):874-881
[42] Shane, R. M., Lynn, W.R.. Mathematical model for food risk evaluation [J].Hydraul. Div.,1964, ASCE90(HY6),1–20.
[43] Madsen, H., Rosbjerg, D., Harremoes, P.. Application of the Bayesian approachin region alanalysis of extreme rainfalls [J]. Stoch. Hydrol. Hydraul.1995,9(1),77–88.
[44] Madsen, H., Pearson, C.P., Rosbjerg, D.. Comparison of annual maximumseries and partial duration series methods for modeling extreme hydrologicalevents2. Regional modeling [J]. Water Resour. Res.1997,33(4),759–769.
[45] n z, B., Bayazit, M.. Effect of occurrence process of the peaks overthreshold on the food estimates [J]. Hydrol,2001.244,86–96.
[46] Bhunya P. K., Singh R. D., Berndtsson R., Panda S. N.. Flood analysis usinggeneralized logistic models in partial duration series [J]. Journal of Hydrology,2011,420–421,59–71.
[47] Fitzgerald, D. L.. Singl estation and regional analysis of daily rainfall extremes[J]. Stoch. Hydrol. Hydraul,1989.3,281–292
[48] Santiago Beguería. Uncertainties in partial duration series modelling ofextremes related to the choice of the threshold value [J]. Preprint of articlepublished in Journal of Hydrology,2005,303:215-230.
[49] P. T. adamson, W. zucchini., on the application of a censored lognormaldistribution to partial duration series, Water SA,1984,10(3):136-146
[50]王善序.超定量洪水系列系列频率分析[J].人民长江,1999(8):23-25
[51] Ekanayake, S. T., Cruise, J. F.. Comparisons of Weibull-and exponential-basedpartial duration stochastic food models. Stoch. Hydrol. Hydraul,1993.7(4),283–297.
[52] Liu T.F. and Ma F.S.. Prediction of Extreme Wave Heights and WindVelocities[J]. Journal of the Waterway Port Coastal and Ocean Division, ASCE,1980,106, No. WW4:469-479
[53] Liu T F. Long Term Distributions of Hurricane Characteristics [C]. Proc.Offshore Technology Conference OTC4325,1982, pp.305-313
[54] Muir L R, El-Shaarawi A H. On the calculation of extreme Wave heights: Areview. Ocean Engineering,1986,13(1):93~118
[55] Kirby W H and Moss M E. Summary of Flood-Frequency Analysis in theUnited States, Journal of Hydrology,1987,96:5~14
[56] Tsimplis M N and Blackman D. Extreme Sea-Level Distribution and ReturnPeriods in the Aegean and lonian Seas. Estuarine. Coastal and Shelf Science,1997,44:79~89
[57] Dong S, Fan D Q, Shi X, et al. Typhoon Storm Surge Intensity GradeClassification in Qingdao Area. OMAE2009, Hawaii,2009,207~211
[58]董胜,刘伟,宁进进.台风波高重现值的泊松最大熵分布估计[J].中国造船，2009,50(4),13-21.
[59]宋艳.多维联合概率的随机模拟技术及其工程应用[D].中国海洋大学，青岛，2004
[60] API RP2A-LRFD. Planning, designing and constructing for fixed offshoreplatforms load and resistance factor design. ISO13819-2,1995,(E)
[61] Loyd’s Register of Shipping. Rules and regulations for the classification of afloating offshore installation at a fixed location. London,1999
[62]中国海洋石油总公司．SY/T10009-1996．海上固定平台规划、设计和建造的推荐作法-荷载和抗力系数设计法．北京：石油工业出版社，1996
[63]刘德辅，韩凤亭，庞亮，王风清.台风作用下核电站海岸防护标准的概率分析[J].中国海洋大学学报，2010，40(6):140~146
[64] Dong S, Liu W, Xu P J. Combination criteria of joint extreme significant waveheight and wind speed at Weizhoudao offshore area. OMAE2008, Estoril, Portugal,2008,241~246.
[65] Yue S. The Gumbel logistic model for representing a multivariate storm event.Advances in Water Resources.2001,24(2):179~185
[66] Tawn, J.A.. Bivariate extreme value theory: Models and estimation. Biometrika,1988.75,397-415.
[67] Tawn, J. A.. Modelling multivariate extreme value distributions [J]. Biometrika,1990,77,245-53.
[68] Coles, S.G., Tawn, J. A.. Modelling extreme multivariate events [J]. Journal ofthe Royal Statistical Society,1991,B53,377-392.
[69] Davison, A. C., Smith, R. L.. Models for exceedances over high thresholds(with discussion)[J]. J. R. Statist. Soc. B,1990,52,393-442.
[70] Coles, S.G., Tawn, J.A.. Statistical methods for multivariate extremes: anapplication to structural design [J]. Applied Statistics,1994,43,1-48.
[71] Ledford A．W．，Tawn J．A．Statistics for near independence in multivariateextreme values[J]．Biometrika，1996,(83)：169-187．
[72] N. Tajvidi, Characterisation and Some Statistical Aspects of Univariate andMultivariate Generalised Pareto Distributions [D], Department of Mathematics,Chalmers Tekniska Hogskola Goteborg,http://www.math.chalmers.se/nader/thesis.ps,1996
[73] H.Rootzen, N.Tajvidi, Multivariate Generalized Pareto Distributions, Preprint,2005:38, Chalmers Tekniska H ogskola G oteborg,http://www.math.chalmers.se/Math/Research/Preprints/2005/38.pdf,2005
[74] Rakonczai. P. Bivariate generalized Paretodistri butionin practice: models andestimation.(submitted) Availableatwww.cs.elte.hu/~paulo/pdf/bgpd_environmetrics.pdf,2011.
[75] E.Kaufmann,R., D.Reiss, Approximation Rates for Multivariate Exceedances,Journal of Statistical Planning and Inference,199545,pp.235-245
[76] M. Falk, J. H usler, R.-D. Reiss, Laws of Small Numbers: Extremes and RareEvents2ndedition [M], Birkhauser Basel,2004
[77]王莉萍，多维复合极值分布理论及其工程应用[D]，青岛：中国海洋大学，2005.
[78] Ross, Sheldon M.. Simulation，San Diego, Calif.:Academic Press,2002,3rded.
[79] Rubinstein R Y. Simulation and Monte Carlo Methods，New York: JohnWiley-Sons,1981
[80]张军，张立红.蒙特卡罗计算机模拟方法及其在石油领域的应用[J].油气田地面工程，2003,9
[81]陈元芳.随机模型参数估计及洪水随机模拟研究[D].河海大学，1992
[82]丁晶.论水文学中的随机模型[A].水文学的回顾与展望论文集[C].南京:河海大学出版社，1988
[83]三峡工程洪水随机模拟研究[S].水利部长江水利委员会水文局，1990.
[84] T. C.Sharma. Stouchastic Models Applied to Evaluating Hydrologic Changes[J]. Journal of Hydrology, l985(78)
[85] Peter J. Hawkes, Ben P. Gouldby，Jonathan A. Tawn, Michael W. Owen. TheJointProbability of Waves and Water Levels in Coastal Engineering Design,Journal of Hydraulicresearch,2002, Vo1.40, No.3，pp241-251.
[86] Fangjun Li, Charlie Bicknell, Reena Lowry, YunLi. A comparison of extremewave analysis methods with1994–2010offshore Perth dataset [J]. CoastalEngineering,2012，69,1-11.
[87] You, Z. J.. Discussion of “a Multi-distribution Approach to POT Methods forDetermining Extreme Wave Heights” by Mazas and Hamm,[Coastal Engineering,58:385–394][J]. Coastal Engineering,2012，61,49–52.
[88] Mazas, F., Hamm, L.. Reply to Discussion by Z. J. You of "A multi-distributionapproach to POT methods for determining extreme waveheights" by Mazas andHamm [J]. Coastal Engineering,2012.65,16–18.
[89] GodaY. On the methodology of Selecting design Wave heinht.21rdInternational Confereneeon Coasta lEngineering. ASCE,1988,899-913.
[90] Goda, Y., Konagay, O.，Takeshita，N., Hitomi，H.，and Naga, T.. Populationdistribution of Extreme Wave Heights Estimated through Regional Analysis,27thInternational Conference on Coastal Engineering, ASCE,2000,1078-1091.
[91] Coles, S. An Introduction of Statistical Modelling of Extreme Values [M].Springer,2001
[92]刘德辅,王莉萍,宋艳,庞亮.复合极值分布理论及其工程应用[J].中国海洋大学学报，2004,34(5):893-902
[93] Todorovic, P. and Zelenhasic, E., A stochastic model for flood analysis [J].Water Resour. Res.,1970,6(6):1641-1648.
[94] Felle,W.,1957. An Introduction to Probability Theory and its Applications.2ndedn, Vol. l. John willey,1982, Newyork.
[95]姜云鹏.港口及海岸工程设计波浪及相应极端荷载分析理论及方法研究[D].中国海洋大学,2007.
[96] Haring, R.E. and J.C.Heideman. Gulf of Mexico Rare Wave Return Periods.OTC3230, offshore Technology Conferenc,1978, Houston, Teexas.
[97] Pickands III, J.. Multivariate extreme value distributions. Proc.43th SessionISI (Buenos Aires),1981, pp.859-878.
[98] Tawn J.A.. Modeling multivariate extreme value distribution [J]. Biometrika,1990,75:397-415
[99] Joe H. Families of min-stable multivariate exponential and multivariateextreme value distributions[J]. Statist. Probab. Lett.,1989,975-82.
[100] McFadden D. Modelling the choice of residential location [R]. In SpatialInteraction Theory and Planning Models, Amsterdam: North-Holland.1978,75-96.
[101] Shi Dao-ji. Moment estimation for multivariate value distribution in a nestedlogistic model [J]. Ann. Inst. Statist. Math,1999,51(2):253-264.
[102]史道济，阮明恕，王毓娥，多元极值分布随机向量的抽样方法[J].应用概率统计，1997，Vol.13，No.1，Pp75-80.
[103]周德才，孙亦鸣.计算机随机模拟原理、方法及计算程序[M],华中理工大学出版社，1998.
[104] Rubinstein R Y. Simulation and Monte Carlo Methods [M]. NewYork: JohnWiley-Sons,1981
[105]林敏.石油价格系统的随机[D].成都：西南石油大学，2006
[106]刘应状,朱耀庭,朱光喜,等.准直激光束水下光斑的变化规律研究[J].中国激光,2001, A28(7):6172620.
[107] Fang K T, Wang Y. Number TheoreticMethods in Statistics[M]. Chapman&Hall, New York,1994
[108]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985:171-186.
[109] G. E. Forsythe and R. A. Leibler. Matrix inversion by a MonteCarlo method,MTAC,1960,4,127-129.a
[110]陈元芳.一种可考虑历史洪水的马氏权函数法的研究[J].水科学进展,1994,5(3):174-178.
[111]陈元芳,沙志贵,等.具有历史洪水时P-Ⅲ线性矩法的研究[J].河海大学学报,2001,29(4):76-80.
[112]林两位,王莉萍.用Pearson-Ⅲ概率分布推算重现期年最大日雨量[J].气象科技，2005，33（4）：314-317.
[113]庞文保等.330kv榆神输电线路最大风速取值的推算[J].气象，1998，24（1）：53～55
[114]屠其璞、王俊德、丁裕国、史慧敏，气象应用概率统计学[M]，192～216，气象出版社，1984
[115] Liu Defu, Wen Shuqin, Wang Liping. Poisson-Gumbel Mixed CompoundDistribution and Its Application [J]. China Science Bulletin,2002,47(22):1901-1904
[116] Liu Defu, Wang L P, Pang L,2006. Theory of multivariate compound extremevalue distribution and its application to extreme sea state prediction [J], ChineseScience Bulletin,51(23):2926-2930.
[117] Liu Defu, Wang Fengqing, Jing Hao, et al,2009. Prediction of typhoontriggered sea hazards in China [C].//Proc OCEANS’09IEEE, OCEANS’090204-001.
[118] Chen Xinu. An Introduction to Change Point Statistical Analysis [J].Application of Statistics and Management,1991,52-59.(in Chinese)
[119]张经汉,李少英.受陆岸影响浅水区台风风浪的计算方案[J].热带海洋，1994，13(1)：25-30.
[120] ZHU Liang-sheng, SONG Yun-fa, QIU Zhang CHEN Xiu-hua, MAI Bo-qiang,QIU Zhang, QIU Yao-wen, SONG Li-li,2003. Computtation of Wave, Tide andWind Current for the South China Sea Under Tripical Cyclones [J]. China OceanEngineering,17(2),550-516.
[121] Stephenson, A.，Simulating multivariate extreme value distributions of logistictype. Extremes,20036,49–59.
[122]史道济,孙炳.嵌套Logistic模型的矩估计[J].系统工程理论与实践,2001,1:53-60.
[123] Shi Dao ji. Moment estimation for multivariate extreme value distribution [J].Applied Mathematics-A Journal of Chinese Universities, B,1995,10:61～68.
[124] Shi Daoji. Multivariate extreme value distribution and its fisher informationmatrix [J]. Acta Mathematicae Applicatae Sinica.(English Series),1995,11:421～428.
[125] Shi Dao ji. Fisher information for a multivariate extreme value distribution [J].Biomet rika,1995,82:664～669.
[126] Shi Dao ji. Moment estimation for multivariate value distribution in a nestedlogistic model [J]. Ann. Inst. Statist. Math,1999,51(2):253-264.
[127] Morton I. D., Bowers J.. Extreme value analysis in a multivariate offshoreenvironment [J]. Applied Ocean Research,1996,18(6):303-317
[128]段忠东，周道成，肖仪清等．极值风浪联合概率模型参数估计的简化方法[J]．哈尔滨建筑大学学报，2002，35（6）：1~5
[129]仇学艳,王超,秦崇仁.多元极值概率分析方法在海洋工程中的应用现状[J].海洋工程,2001,19(3):91-95.
[2]梁飞.海洋经济和海洋可持续发展理论方法及其应用研究[D].天津大学，2003
[3]储永萍，蒙少东.发达国家海洋经济发展战略及对中国的启示[J].湖南农业科学,2009，（8）：154~157.
[4] IPCC. Climate change2007: The scientifie basis. Cambridge university Press，Cambridge，2007.
[5] P.J.Webster,G.J.Holland,J.A.Curry,H.-R.Chang. Changes in Tropical CycloneNumber, Duration, and Intensity in a Warming Environment [J], Science,2005,309:1844-1846.
[6]林而达，许吟隆，蒋金荷等.气候变化国家评估报告(II):气候变化的影响与适应.气候变化研究进展，2006，2(2):5-56.
[7]国家海洋局，2003年中国海平面公报.
[8]国家海洋局，2009年中国海平面公报.
[9]中国科学院地学部．海平面上升对中国三角洲地区的影响及对策.北京：科学出版社，1994：1～12
[10]国家海洋局，2012年中国海平面公报。
[11]殷杰．中国沿海台风风暴潮灾害风险评估研究[D]．华东师范大学，2011
[12]杨桂山，施雅风．西北太平洋热带气旋频数的变化及与海表温度的相关研究[J].地理学报，1993，54(l):22-2
[13]施雅风.我国海岸带灾害的加剧发展及其防御方略[J].自然灾害学报，1994，3(2)：3～l.
[14]杨桂山．中国沿海风暴潮灾害的历史变化及未来趋向[J].自然灾害学报，2000，9(3):23-30.
[15]夏东兴，刘振夏等．渤海湾西岸海平面上升威胁的防治对策[J]．自然灾害学报，1993，2(1)：48~52
[16]朱季文，季子修等．海平面上升对长江三角洲及邻近地区的影响[J].地理科学，1995，1(2)：109~11.
[17]何洪，邹斌等.海平面上升对广东沿海风暴期灾害的可能影响[J].广东省水文总站，1995.
[18]季子修，施雅风.海平面上升、海岸带灾害与海岸防护问题[J].自然灾害学报，1996,5(2):56-64
[19]陆儒德.海洋-国家-海权[M].北京:海潮出版社,2000.135-180.
[20]李玉成.海洋工程技术进展与对发展我国海洋经济的思考[J].大连理工大学学报,2002,42(1):1-5.
[21]国家海洋局，2012中国海洋灾害公报,2012
[22]邓松，刘雪峰，游大伟等．广东省1991-2005年５种主要海洋灾害概况[J]．广东气象．2006，11(4):19-22．
[23]国家海洋局，2009年中国海洋灾害公报
[24]刘文通，楼顺里，宋珊，吴术礼.海上石油平台对周围风场的影响[J].青岛海洋大学学报，1990，20(3)：39-48.
[25]赵声振．对我国海上边际油田开发价值预期的反思．国际石油经济，2004，12（8）：50~60.
[26]刘伟.基于最大熵分布的海洋平台环境条件联合重现值推算[D].中国海洋大学，青岛，2011.
[27]黎志均，刘永锋．台风“伊布都“对珠海市桂山岛某湾东防波堤损坏原因分析[J]．中国港湾建设.2006，5：10-11．
[28]董胜,李奉利,孙瑞文.风暴增水随机分析的过阈法及其统计计算模式[J].青岛海洋大学学报,2000,30(3):542-548.
[29] Guedes Seares C． and T． Moan． On the uncertainties to the extremehydrodynamic loading of a cylindrical plie，reliability theory and its application[J]，Structural&Soil Mechanics，Mantiuns Nijhoft Pub．，Hague，l983．351-364．
[30] Haver S., Analysis of uncertainties related to the stochastic modelling of oceanwaves [J]．Report UR8009．NTH l980，183-186．
[31] Haver S., T. Moan．On some uncertainties related to the short-term stochasticmodelling of ocean waves [J]，Applied Ocean Research，5(1983),2:35-48．
[32] Liu Teh fu, wang Chao, Design wave criteria for offshore structures[J]．offshore Mechanics and Arctic Engineering,1986,1:404-409．
[33] Leese, M.N.. Use of censored data in the estimation of the Gumbel distributionparameters for annual maximum flood series [J], Water Resour. Res.,1973,9(6),1534-1542.
[34] Francés, F., J.D. Salas and D.C. Boes. Flood frequency analysis withsystematic and historical or paleoflood data based on the two-parameter generalextreme value models [J]. Water Resour. Res.,1994,30(6),1653-1664.
[35] YOU Zai-jin, YIN Bao-shu. Estimation of Extreme Coastal Wave HeightsFrom Time Series of Wave Data [J]. China Ocean Engineering,2006，20(2),225-241
[36]马逢时，刘德辅.复合极值分布理论及其应用.应用数学学报，1979,2(4),365-375.
[37]交通部第一航务工程勘察设计院.海港水文规范．JTJ213-98北京：人民交通出版社，1998.
[38] NERC. Flood Studies Report, Vol.1,1975. Natural Enviroment ResearchCouncil, London.
[39]史道济.实用极值统计方法[M].天津:天津科学技术出版社,2006
[40] Mazas, F., Hamm, L.. A multi-distribution approach to POT methods fordetermining extreme wave heights [J]. Coastal Engineering,2011，58,385–394.
[41]王超,刘德辅.设计波浪选取中的不确定性分析[J].海洋学报，1991，13(4):874-881
[42] Shane, R. M., Lynn, W.R.. Mathematical model for food risk evaluation [J].Hydraul. Div.,1964, ASCE90(HY6),1–20.
[43] Madsen, H., Rosbjerg, D., Harremoes, P.. Application of the Bayesian approachin region alanalysis of extreme rainfalls [J]. Stoch. Hydrol. Hydraul.1995,9(1),77–88.
[44] Madsen, H., Pearson, C.P., Rosbjerg, D.. Comparison of annual maximumseries and partial duration series methods for modeling extreme hydrologicalevents2. Regional modeling [J]. Water Resour. Res.1997,33(4),759–769.
[45] n z, B., Bayazit, M.. Effect of occurrence process of the peaks overthreshold on the food estimates [J]. Hydrol,2001.244,86–96.
[46] Bhunya P. K., Singh R. D., Berndtsson R., Panda S. N.. Flood analysis usinggeneralized logistic models in partial duration series [J]. Journal of Hydrology,2011,420–421,59–71.
[47] Fitzgerald, D. L.. Singl estation and regional analysis of daily rainfall extremes[J]. Stoch. Hydrol. Hydraul,1989.3,281–292
[48] Santiago Beguería. Uncertainties in partial duration series modelling ofextremes related to the choice of the threshold value [J]. Preprint of articlepublished in Journal of Hydrology,2005,303:215-230.
[49] P. T. adamson, W. zucchini., on the application of a censored lognormaldistribution to partial duration series, Water SA,1984,10(3):136-146
[50]王善序.超定量洪水系列系列频率分析[J].人民长江,1999(8):23-25
[51] Ekanayake, S. T., Cruise, J. F.. Comparisons of Weibull-and exponential-basedpartial duration stochastic food models. Stoch. Hydrol. Hydraul,1993.7(4),283–297.
[52] Liu T.F. and Ma F.S.. Prediction of Extreme Wave Heights and WindVelocities[J]. Journal of the Waterway Port Coastal and Ocean Division, ASCE,1980,106, No. WW4:469-479
[53] Liu T F. Long Term Distributions of Hurricane Characteristics [C]. Proc.Offshore Technology Conference OTC4325,1982, pp.305-313
[54] Muir L R, El-Shaarawi A H. On the calculation of extreme Wave heights: Areview. Ocean Engineering,1986,13(1):93~118
[55] Kirby W H and Moss M E. Summary of Flood-Frequency Analysis in theUnited States, Journal of Hydrology,1987,96:5~14
[56] Tsimplis M N and Blackman D. Extreme Sea-Level Distribution and ReturnPeriods in the Aegean and lonian Seas. Estuarine. Coastal and Shelf Science,1997,44:79~89
[57] Dong S, Fan D Q, Shi X, et al. Typhoon Storm Surge Intensity GradeClassification in Qingdao Area. OMAE2009, Hawaii,2009,207~211
[58]董胜,刘伟,宁进进.台风波高重现值的泊松最大熵分布估计[J].中国造船，2009,50(4),13-21.
[59]宋艳.多维联合概率的随机模拟技术及其工程应用[D].中国海洋大学，青岛，2004
[60] API RP2A-LRFD. Planning, designing and constructing for fixed offshoreplatforms load and resistance factor design. ISO13819-2,1995,(E)
[61] Loyd’s Register of Shipping. Rules and regulations for the classification of afloating offshore installation at a fixed location. London,1999
[62]中国海洋石油总公司．SY/T10009-1996．海上固定平台规划、设计和建造的推荐作法-荷载和抗力系数设计法．北京：石油工业出版社，1996
[63]刘德辅，韩凤亭，庞亮，王风清.台风作用下核电站海岸防护标准的概率分析[J].中国海洋大学学报，2010，40(6):140~146
[64] Dong S, Liu W, Xu P J. Combination criteria of joint extreme significant waveheight and wind speed at Weizhoudao offshore area. OMAE2008, Estoril, Portugal,2008,241~246.
[65] Yue S. The Gumbel logistic model for representing a multivariate storm event.Advances in Water Resources.2001,24(2):179~185
[66] Tawn, J.A.. Bivariate extreme value theory: Models and estimation. Biometrika,1988.75,397-415.
[67] Tawn, J. A.. Modelling multivariate extreme value distributions [J]. Biometrika,1990,77,245-53.
[68] Coles, S.G., Tawn, J. A.. Modelling extreme multivariate events [J]. Journal ofthe Royal Statistical Society,1991,B53,377-392.
[69] Davison, A. C., Smith, R. L.. Models for exceedances over high thresholds(with discussion)[J]. J. R. Statist. Soc. B,1990,52,393-442.
[70] Coles, S.G., Tawn, J.A.. Statistical methods for multivariate extremes: anapplication to structural design [J]. Applied Statistics,1994,43,1-48.
[71] Ledford A．W．，Tawn J．A．Statistics for near independence in multivariateextreme values[J]．Biometrika，1996,(83)：169-187．
[72] N. Tajvidi, Characterisation and Some Statistical Aspects of Univariate andMultivariate Generalised Pareto Distributions [D], Department of Mathematics,Chalmers Tekniska Hogskola Goteborg,http://www.math.chalmers.se/nader/thesis.ps,1996
[73] H.Rootzen, N.Tajvidi, Multivariate Generalized Pareto Distributions, Preprint,2005:38, Chalmers Tekniska H ogskola G oteborg,http://www.math.chalmers.se/Math/Research/Preprints/2005/38.pdf,2005
[74] Rakonczai. P. Bivariate generalized Paretodistri butionin practice: models andestimation.(submitted) Availableatwww.cs.elte.hu/~paulo/pdf/bgpd_environmetrics.pdf,2011.
[75] E.Kaufmann,R., D.Reiss, Approximation Rates for Multivariate Exceedances,Journal of Statistical Planning and Inference,199545,pp.235-245
[76] M. Falk, J. H usler, R.-D. Reiss, Laws of Small Numbers: Extremes and RareEvents2ndedition [M], Birkhauser Basel,2004
[77]王莉萍，多维复合极值分布理论及其工程应用[D]，青岛：中国海洋大学，2005.
[78] Ross, Sheldon M.. Simulation，San Diego, Calif.:Academic Press,2002,3rded.
[79] Rubinstein R Y. Simulation and Monte Carlo Methods，New York: JohnWiley-Sons,1981
[80]张军，张立红.蒙特卡罗计算机模拟方法及其在石油领域的应用[J].油气田地面工程，2003,9
[81]陈元芳.随机模型参数估计及洪水随机模拟研究[D].河海大学，1992
[82]丁晶.论水文学中的随机模型[A].水文学的回顾与展望论文集[C].南京:河海大学出版社，1988
[83]三峡工程洪水随机模拟研究[S].水利部长江水利委员会水文局，1990.
[84] T. C.Sharma. Stouchastic Models Applied to Evaluating Hydrologic Changes[J]. Journal of Hydrology, l985(78)
[85] Peter J. Hawkes, Ben P. Gouldby，Jonathan A. Tawn, Michael W. Owen. TheJointProbability of Waves and Water Levels in Coastal Engineering Design,Journal of Hydraulicresearch,2002, Vo1.40, No.3，pp241-251.
[86] Fangjun Li, Charlie Bicknell, Reena Lowry, YunLi. A comparison of extremewave analysis methods with1994–2010offshore Perth dataset [J]. CoastalEngineering,2012，69,1-11.
[87] You, Z. J.. Discussion of “a Multi-distribution Approach to POT Methods forDetermining Extreme Wave Heights” by Mazas and Hamm,[Coastal Engineering,58:385–394][J]. Coastal Engineering,2012，61,49–52.
[88] Mazas, F., Hamm, L.. Reply to Discussion by Z. J. You of "A multi-distributionapproach to POT methods for determining extreme waveheights" by Mazas andHamm [J]. Coastal Engineering,2012.65,16–18.
[89] GodaY. On the methodology of Selecting design Wave heinht.21rdInternational Confereneeon Coasta lEngineering. ASCE,1988,899-913.
[90] Goda, Y., Konagay, O.，Takeshita，N., Hitomi，H.，and Naga, T.. Populationdistribution of Extreme Wave Heights Estimated through Regional Analysis,27thInternational Conference on Coastal Engineering, ASCE,2000,1078-1091.
[91] Coles, S. An Introduction of Statistical Modelling of Extreme Values [M].Springer,2001
[92]刘德辅,王莉萍,宋艳,庞亮.复合极值分布理论及其工程应用[J].中国海洋大学学报，2004,34(5):893-902
[93] Todorovic, P. and Zelenhasic, E., A stochastic model for flood analysis [J].Water Resour. Res.,1970,6(6):1641-1648.
[94] Felle,W.,1957. An Introduction to Probability Theory and its Applications.2ndedn, Vol. l. John willey,1982, Newyork.
[95]姜云鹏.港口及海岸工程设计波浪及相应极端荷载分析理论及方法研究[D].中国海洋大学,2007.
[96] Haring, R.E. and J.C.Heideman. Gulf of Mexico Rare Wave Return Periods.OTC3230, offshore Technology Conferenc,1978, Houston, Teexas.
[97] Pickands III, J.. Multivariate extreme value distributions. Proc.43th SessionISI (Buenos Aires),1981, pp.859-878.
[98] Tawn J.A.. Modeling multivariate extreme value distribution [J]. Biometrika,1990,75:397-415
[99] Joe H. Families of min-stable multivariate exponential and multivariateextreme value distributions[J]. Statist. Probab. Lett.,1989,975-82.
[100] McFadden D. Modelling the choice of residential location [R]. In SpatialInteraction Theory and Planning Models, Amsterdam: North-Holland.1978,75-96.
[101] Shi Dao-ji. Moment estimation for multivariate value distribution in a nestedlogistic model [J]. Ann. Inst. Statist. Math,1999,51(2):253-264.
[102]史道济，阮明恕，王毓娥，多元极值分布随机向量的抽样方法[J].应用概率统计，1997，Vol.13，No.1，Pp75-80.
[103]周德才，孙亦鸣.计算机随机模拟原理、方法及计算程序[M],华中理工大学出版社，1998.
[104] Rubinstein R Y. Simulation and Monte Carlo Methods [M]. NewYork: JohnWiley-Sons,1981
[105]林敏.石油价格系统的随机[D].成都：西南石油大学，2006
[106]刘应状,朱耀庭,朱光喜,等.准直激光束水下光斑的变化规律研究[J].中国激光,2001, A28(7):6172620.
[107] Fang K T, Wang Y. Number TheoreticMethods in Statistics[M]. Chapman&Hall, New York,1994
[108]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985:171-186.
[109] G. E. Forsythe and R. A. Leibler. Matrix inversion by a MonteCarlo method,MTAC,1960,4,127-129.a
[110]陈元芳.一种可考虑历史洪水的马氏权函数法的研究[J].水科学进展,1994,5(3):174-178.
[111]陈元芳,沙志贵,等.具有历史洪水时P-Ⅲ线性矩法的研究[J].河海大学学报,2001,29(4):76-80.
[112]林两位,王莉萍.用Pearson-Ⅲ概率分布推算重现期年最大日雨量[J].气象科技，2005，33（4）：314-317.
[113]庞文保等.330kv榆神输电线路最大风速取值的推算[J].气象，1998，24（1）：53～55
[114]屠其璞、王俊德、丁裕国、史慧敏，气象应用概率统计学[M]，192～216，气象出版社，1984
[115] Liu Defu, Wen Shuqin, Wang Liping. Poisson-Gumbel Mixed CompoundDistribution and Its Application [J]. China Science Bulletin,2002,47(22):1901-1904
[116] Liu Defu, Wang L P, Pang L,2006. Theory of multivariate compound extremevalue distribution and its application to extreme sea state prediction [J], ChineseScience Bulletin,51(23):2926-2930.
[117] Liu Defu, Wang Fengqing, Jing Hao, et al,2009. Prediction of typhoontriggered sea hazards in China [C].//Proc OCEANS’09IEEE, OCEANS’090204-001.
[118] Chen Xinu. An Introduction to Change Point Statistical Analysis [J].Application of Statistics and Management,1991,52-59.(in Chinese)
[119]张经汉,李少英.受陆岸影响浅水区台风风浪的计算方案[J].热带海洋，1994，13(1)：25-30.
[120] ZHU Liang-sheng, SONG Yun-fa, QIU Zhang CHEN Xiu-hua, MAI Bo-qiang,QIU Zhang, QIU Yao-wen, SONG Li-li,2003. Computtation of Wave, Tide andWind Current for the South China Sea Under Tripical Cyclones [J]. China OceanEngineering,17(2),550-516.
[121] Stephenson, A.，Simulating multivariate extreme value distributions of logistictype. Extremes,20036,49–59.
[122]史道济,孙炳.嵌套Logistic模型的矩估计[J].系统工程理论与实践,2001,1:53-60.
[123] Shi Dao ji. Moment estimation for multivariate extreme value distribution [J].Applied Mathematics-A Journal of Chinese Universities, B,1995,10:61～68.
[124] Shi Daoji. Multivariate extreme value distribution and its fisher informationmatrix [J]. Acta Mathematicae Applicatae Sinica.(English Series),1995,11:421～428.
[125] Shi Dao ji. Fisher information for a multivariate extreme value distribution [J].Biomet rika,1995,82:664～669.
[126] Shi Dao ji. Moment estimation for multivariate value distribution in a nestedlogistic model [J]. Ann. Inst. Statist. Math,1999,51(2):253-264.
[127] Morton I. D., Bowers J.. Extreme value analysis in a multivariate offshoreenvironment [J]. Applied Ocean Research,1996,18(6):303-317
[128]段忠东，周道成，肖仪清等．极值风浪联合概率模型参数估计的简化方法[J]．哈尔滨建筑大学学报，2002，35（6）：1~5
[129]仇学艳,王超,秦崇仁.多元极值概率分析方法在海洋工程中的应用现状[J].海洋工程,2001,19(3):91-95.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |