An expectation-maximization scheme for measurement error models

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• 作者：Anindya Bhadra ^{bhadra@purdue.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Expectation&ndash ; maximization ; Maximum likelihood ; Measurement error ; Splines
• 刊名：Statistics & Probability Letters
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：120
• 期：Complete
• 页码：61-68
• 全文大小：400 K

文摘

We treat the problem of maximum likelihood estimation in measurement error models. Direct maximization of the analytically intractable likelihood in such models is difficult. We derive an efficient expectation–maximization scheme for truncated polynomial spline models of degree one. Simulation results confirm the effectiveness of the proposed method.