کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5129375 1489642 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonparametric tests for multi-parameter M-estimators
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Nonparametric tests for multi-parameter M-estimators
چکیده انگلیسی

We consider likelihood ratio like test statistics based on M-estimators for multi-parameter hypotheses for some commonly used parametric models where the assumptions on which the standard test statistics are based are not justified. The nonparametric test statistics are based on empirical exponential families and permit us to give bootstrap methods for the tests. We further consider saddlepoint approximations to the tail probabilities used in these tests. This generalizes earlier work of Robinson et al. (2003) in two ways. First, we generalize from bootstraps based on resampling vectors of both response and explanatory variables to include bootstrapping residuals for fixed explanatory variables, resulting in a surprising result for the weighted resampling. Second, we obtain a theorem for tail probabilities under weak conditions providing essential justification for the approximation to bootstrap results for both cases. We use as examples linear regression, non-linear regression and generalized linear models under models with independent and identically distributed residuals or vectors of observations, giving numerical illustrations of the results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 158, June 2017, Pages 103-116
نویسندگان
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