On eliminating the asymptotic bias in the quasi-least squares estimate of the correlation parameter
On eliminating the asymptotic bias in the quasi-least squares estimate of the correlation parameter
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DOI：
10.1016/s0378-3758(98)00180-3
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发表时间：
1999-02-01
影响因子：
0.9
通讯作者：
Shults, J
中科院分区：
文献类型：
--
作者：
Chaganty, NR;Shults, J
在最近的一篇论文中，Chaganty (1997, J. Statist. Plann. Inference 63, 39-54) 介绍了拟最小二乘 (QLS) 方法，用于估计纵向数据分析问题中的回归、相关性和尺度参数。即使工作相关结构指定错误，回归和尺度参数的 QLS 估计也是一致的。然而，相关参数的估计是渐近偏差的。在本文中，我们提出了以下工作相关结构的相关参数的修改（C-QLS）估计，这些结构适用于平衡和等距纵向数据的分析：非结构化矩阵，其中 C-QLS 估计是正定的一致相关矩阵；以及可交换、三对角和自回归结构，对于这些结构，C-QLS 估计是可行的、一致的并且对错误指定具有鲁棒性。我们还提出了适用于分析不平衡和不等间隔纵向数据的两种结构的可行且一致的 C-QLS 估计：Nunez-Anton 和 Woodworth (1994, Biometrics 50, 445-456) 以及 Shults 和 Chaganty (1998, Biometrics 54, 1622-1630) 讨论的马尔可夫和广义马尔可夫工作相关结构。然后，我们提出了一种改进的尺度参数一致估计。最后，举例将 C-QLS 估计值与使用广泛使用的广义估计方程 (GEE) 方法获得的估计值进行对比。 (C) 1999 Elsevier Science B.V. 保留所有权利。
In a recent paper, Chaganty (1997, J. Statist. Plann. Inference 63, 39-54) introduced the method of quasi-least squares (QLS) for estimating the regression, correlation and scale parameters in longitudinal data analysis problems. The QLS estimates of the regression and scale parameters are consistent even if the working correlation structure is misspecified. The estimate of the correlation parameter, however, is asymptotically biased. In this paper, we present modified (C-QLS) estimates of the correlation parameter for the following working correlation structures that are appropriate for the analysis of balanced and equally spaced longitudinal data: the unstructured matrix, for which the C-QLS estimate is a positive definite, consistent correlation matrix; and the exchangeable, tridiagonal, and autoregressive structures, for which the C-QLS estimates are feasible, consistent and robust against misspecification. We also present feasible and consistent C-QLS estimates for two structures appropriate for the analysis of unbalanced and unequally spaced longitudinal data: the Markov and generalized Markov working correlation structures that were discussed by Nunez-Anton and Woodworth (1994, Biometrics 50, 445-456) and Shults and Chaganty (1998, Biometrics 54, 1622-1630). We then present an improved consistent estimate of the scale parameter. Finally, examples are given to contrast the C-QLS estimates with estimates obtained using the widely used generalized estimating equation (GEE) approach. (C) 1999 Elsevier Science B.V. All rights reserved.