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作者：Gabriel, KR
作者单位：University of Rochester
摘要：This paper discusses the application of generalised linear methods to bilinear models by criss-cross regression. It proposes an extension to segmented bilinear models in which the expectation matrix is linked to a sum in which each segment has specified row and column covariance matrices as well as a coefficient parameter matrix that is specified only by its rank. This extension includes a variety of biadditive models including the generalised Tukey degree of freedom for non-additivity model t...
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作者：Basu, A; Harris, IR; Hjort, NL; Jones, MC
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Northern Arizona University; University of Oslo; Open University - UK
摘要：A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such as minimum Hellinger distance estimation, avoid the use of nonparametric density estimation and associated complications such as bandwidth selection. The pro; posed class of 'density power divergences' is indexed by a single parameter alpha which controls the trade-off between robustness and efficiency. Th...
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作者：Troxel, AB; Lipsitz, SR; Harrington, DP
作者单位：Columbia University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：We propose methods for the analysis of continuous responses subject to nonignorable non-monotone missing data. We form a pseudolikelihood by naively assuming independence over time and using a product of marginal likelihoods at each time point, and we obtain consistent and asymptotically normal estimators of the mean and missingness parameters. Our primary interest is in estimating the parameters of the marginal model at each time point, and we make no assumption about the correlation structur...
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作者：Shi, P; Fung, WK
作者单位：Peking University; University of Hong Kong
摘要：For the problem of choosing a transformation h(y) of a univariate response variable y to achieve the linearity of the regression function E{h(y)\x}, we view Cook & Weisberg's (1994) method as an iterative procedure and estimate the transformed linear model based on the fixed point of the iteration procedure. When the procedure is implemented with B-spline smoothing by projecting the function h(y) into a B-spline space, it is proved that the fixed point is identical to the solution obtained fro...
