Accurate Inference through Bias Correction for Parametric and Semiparametric Models

Diplôme Thèse Accurate Inference through Bias Correction for Parametric and Semiparametric Models eng 2024-08-05 2024-08-05 author Zhang Yuming 0000-0003-2293-9601 910595 Recherche et enseignement GSEM (RESEM) director Victoria-Feser Maria-Pia 23194 Recherche et enseignement GSEM (RESEM) 186 10.13097/archive-ouverte/unige:179354 urn:nbn:ch:unige-1793547 GSEM 145 Technological innovations have enabled the collection of massive amounts of complex data, posing new challenges for achieving accurate statistical inference. In this context, this thesis aims to develop statistical methods that can achieve accurate inference when handling large and complex data. It consists of two chapters, each presenting methodological developments for parametric and semiparametric models. In the first chapter, we introduce an indirect inference based method that can achieve accurate inference for general parametric models, including those where tractable estimators are lacking. The key advantage of this method is that it provides a simple strategy to construct a consistent estimator in a computationally efficient manner, all under the guarantee of a sharp bias bound that yields accurate inference. This method is particularly useful for handling challenging data features (e.g., misclassification, censoring) and constructing robust estimators. The practical usefulness and superior finite sample performance of our proposed method are illustrated in simulation studies and a real data analysis on alcohol consumption. In the second chapter, we extend the method developed in the first chapter to semiparametric generalized partially linear models. Our proposed estimator is shown to be consistent, asymptotically normal, and semiparametric efficient. More importantly, we show that the proposed estimator has a smaller bias order than standard estimators, which leads to a significant improvement in inference accuracy for both the parametric and nonparametric components. We present simulation studies and a real data analysis on early-stage diabetes to showcase the superior finite sample performance of our proposed method. This methodological development brings forth the importance of bias correction in inference for semiparametric models, providing valuable insights for further potential extensions, such as in survival analysis. Dewey 330/650 9 Faculté d'économie et de management S false b959bb7c-cfc9-412a-9636-de97eb3a456f Thesis Restricted PhD_Thesis_YumingZhang_240812_final.pdf 1683882 application/pdf 8e8173b53b04808b81d99531e6b3888c89f9ae4be0d7a9300aadb79872aeae76 f38f60e6-3610-444c-8169-b82a12254c6d Imprimatur Public ZHANG_Yuming_Imprimatur.pdf 83566 application/pdf d3f9a8de89af5118f28f414edbac6f1b81cd2441c30a1b6e8380ab25c8582f05 restrictedAccess Faculté d'économie et de management

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Accurate Inference through Bias Correction for Parametric and Semiparametric Models				2024	344	8

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