离开可分裂
发表于 2025-3-25 05:06:28
http://reply.papertrans.cn/16/1564/156351/156351_21.png
HEAVY
发表于 2025-3-25 11:29:26
http://reply.papertrans.cn/16/1564/156351/156351_22.png
我邪恶
发表于 2025-3-25 12:39:54
http://reply.papertrans.cn/16/1564/156351/156351_23.png
fastness
发表于 2025-3-25 19:42:20
2191-544X es of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields..978-981-13-6240-8978-981-13-6241-5Series ISSN 2191-544X Series E-ISSN 2191-5458
Enteropathic
发表于 2025-3-25 20:45:53
Introduction to Double-Truncation, arising from economics, medicine and engineering. After discussing the issues of sampling bias due to double-truncation, we briefly review likelihood-based inference methods for doubly truncated data. We finally compare double-truncation with interval/right censoring.
Ambulatory
发表于 2025-3-26 01:21:28
Parametric Estimation Under Exponential Family,SEF). We introduce specific models in the SEF, and computational algorithms for maximum likelihood estimators (MLEs) under these models. We review the asymptotic theory for the MLE and then give the standard error and confidence interval. We also introduce an R package “double.truncation” (Emura et
Congruous
发表于 2025-3-26 05:49:26
Bayesian Inference for Doubly Truncated Data,ocess of units (i.e. the process which describes the emergence of units in the latent population), whose behaviour might change throughout time, is relevant for statistical inference. In this chapter, a Bayesian approach to double-truncation is developed which allows for piecewise constant process i
星星
发表于 2025-3-26 09:40:15
http://reply.papertrans.cn/16/1564/156351/156351_28.png
nauseate
发表于 2025-3-26 16:03:33
Linear Regression Under Random Double-Truncation, is argued that the conventional OLS estimator is not valid when truncation is present. Instead, a fundamental property of the regression equation is used to construct a non-parametric plug-in-type estimator. The method is based on the NPMLE which is treated in Chap. . (see also Efron and Petrosian
EWER
发表于 2025-3-26 18:42:49
8楼