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• W2779486087 abstract "Standard generalized linear models (GLMs) consist of three components: random component referring to a distribution of the response variable that belongs to the exponential family; systematic component referring to the linear predictor; and known link function specifying the relationship between the linear predictor and the mean of the distribution function. A flexible extension of the standard GLMs allows an unknown link function. Classical parametric likelihood approach is not applicable due to a large parameter space. To address this issue, sieve maximum likelihood estimation has been developed in literature in which the estimator of the unknown link function is assumed to lie in a sieve space. Various methods of sieves including the B-spline and P-spline based methods are introduced. The numerical implementation and theoretical properties of these methods are also discussed. WIREs Comput Stat 2018, 10:e1425. doi: 10.1002/wics.1425 This article is categorized under: Applications of Computational Statistics > Signal and Image Processing and Coding Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical Models > Generalized Linear Models Algorithms and Computational Methods > Maximum Likelihood Methods" @default.
• W2779486087 created "2018-01-05" @default.
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• W2779486087 date "2017-12-22" @default.
• W2779486087 modified "2023-09-24" @default.
• W2779486087 title "Sieve maximum likelihood estimation in generalized linear models with an unknown link function" @default.
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• W2779486087 doi "https://doi.org/10.1002/wics.1425" @default.
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