Generalized Pearson-Fisher Chi-Square Goodness-of-Fit Tests, with Applications to Models with Life History Data
نویسندگان
چکیده
منابع مشابه
Comparison of the Goodness-of-Fit Tests: the Pearson Chi-square and Kolmogorov-Smirnov Tests
A test for goodness of fit usually involves examining a random sample from some unknown distribution in order to test the null hypothesis that the unknown distribution function is in fact a known, specified function. The Chi-square test can be applied to any univariate distribution for which you can calculate the cumulative distribution function. The Chi-square test does not have good propertie...
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The aim of this paper is to relate and extend some recent work on chi-square goodness-of-fit tests. There is no discussion of any problems which are specifically associated with more than one categorical variable.. The main topics are the effect of estimation on chi-square and its partitions ~~d their relation to Neymants smooth goodness-of-fit test, and the effect of grotp:ing a univariate dis...
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Scientists often use the Chi-square (χ) test to determine the “goodness of fit” between theoretical and experimental data. In this test, we compare observed values with theoretical or expected values. Observed values are those that the researcher obtains empirically through direct observation. The theoretical or expected values are developed on the basis of an established theory or a working hy...
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In this chapter, we consider some chi-square-type goodness-of-fit tests for right AQ1 censored samples, viz. the Nikulin–Rao–Robson (NRR) test and generalized Pearson–Fisher (GPF) test. We compare these tests in terms of power in the case of testing composite hypotheses by randomly censored and Type II censored samples. Various grouping methods are considered, including equifrequent grouping, g...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1993
ISSN: 0090-5364
DOI: 10.1214/aos/1176349151