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Journal ArticleDOI

»Smooth test» for goodness of fit

Jerzy Neyman
- 01 Jul 1937 - 
- Vol. 1937, pp 149-199
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TLDR
In this article, Pearson's test for goodness of fit is dedicated to the memory of Karl Pearson (27 March 1857-27 April 1936) who originated the problem of a test for fit and was first to advance its solution.
Abstract
Dedicated to the memory of Karl Pearson (27 March 1857—27 April 1936) who originated the problem of a test for goodness of fit and was first to advance its solution.

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Citations
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Posterior predictive assessment of model fitness via realized discrepancies

TL;DR: In this article, the authors consider Bayesian counterparts of the classical tests for good-ness of fit and their use in judging the fit of a single Bayesian model to the observed data.
Book

Elements of large-sample theory

TL;DR: The most useful parts of large-sample theory are accessible to scientists outside statistics and certainly to master's-level statistics students who ignore most of measure theory as discussed by the authors, which constitutes a coherent body of concepts and results that are central to both theoretical and applied statistics.
Journal ArticleDOI

Generalized likelihood ratio statistics and Wilks phenomenon

TL;DR: The generalized likelihood ratio statistics are shown to be general and powerful for nonparametric testing problems based on function estimation and can even be adaptively optimal in the sense of Spokoiny by using a simple choice of adaptive smoothing parameter.
Journal ArticleDOI

Nonparametric model checks for regression

TL;DR: In this article, a marked empirical process based on residuals is studied, and results on its large-sample behavior may be used to provide nonparametric full-model checks for regression, and their decomposition into principal components gives new insight into the question: which kind of departure from a hypothetical model may be well detected by residual-based goodness-offit methods?
Book ChapterDOI

Asymptotically Optimal Tests for Multinomial Distributions

TL;DR: In this paper, it was shown that chi-square tests of simple and composite hypotheses are inferior to the corresponding likelihood ratio tests, provided that α → 0 at a suitable rate.
References
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On the composition of elementary errors

TL;DR: In this paper, the authors define a variable V(t) the probability function of a quantity z, which may assume certain real values with certain probabilistic properties, and call V t the probability of z having exactly the value t.
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On the composition of elementary errors: Second paper: Statistical applications

TL;DR: In this article, the authors defined the probability function of a statistical variable W(x) as defined in the first paper 1, Art. 1, Art. 7, where m is supposed to be zero and the standard deviation 1.
Journal ArticleDOI

Sur la vérification des hypothèses statistiques composées

TL;DR: The Bulletin de la S. M. F. as mentioned in this paper implique l'accord avec les conditions générales d'utilisation (http://www.numdam.org/legal.html).
Journal ArticleDOI

Free functions and the ≫student≫-fisher theorem

TL;DR: The notion of Free Functions as conceived by Thiele in whose hands it has proved such a powerful instrument of research, is from the outset confined to linear combinations of observations, satisfying certain conditions as mentioned in this paper.
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