Journal ArticleDOI
A Comparative Study of Some Modified Chi-Squared Tests
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TLDR
It is shown that power of test statistic essentially depends on the quantity of Fisher's sample information this statistic uses, and some recommendations on implementing modified chi-squared type tests are given.Abstract:
Some recent results in the theory and applications of modified chi-squared goodness-of-fit tests are briefly discussed. It seems that for the first time power of modified chi-squared type tests for the logistic and three-parameter Weibull distributions based on moment type estimators is studied. Power of different modified tests against some alternatives for equiprobable fixed or random grouping intervals, and for Neyman–Pearson classes is investigated. It is shown that power of test statistic essentially depends on the quantity of Fisher's sample information this statistic uses. Some recommendations on implementing modified chi-squared type tests are given.read more
Citations
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Journal ArticleDOI
Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta‐analysis
D. J. Fan,Wentian He,Ward Smith,Craig F. Drury,Hong Jiang,Brian Grant,Yaoyao Shi,Daping Song,Yanhua Chen,Xuexia Wang,Ping He,Guoyuan Zou +11 more
TL;DR: In this article , the authors synthesized 182 studies (222 sites) worldwide to evaluate the impacts of inhibitors (urease inhibitors, nitrification inhibitors, and combined inhibitors) on crop yields and gaseous N loss (ammonia [NH3] and nitrous oxide [N2O] emissions) and explored their responses to different management and environmental factors.
Journal ArticleDOI
Goodness-of-fit tests for the power-generalized weibull probability distribution
TL;DR: Modified chi-squared tests based on maximum likelihood estimators of parameters that are shown to be -consistent are proposed, and it is proposed to use the left-tailed rejection region because these tests are biased with respect to the above alternatives if one will use the right-tailed rejected region.
Dissertation
Dynamic regression models and their applications in survival and reliability analysis
TL;DR: In this article, a generalized chi-squared test statistic (Y2n) was proposed to fit the survival and reliability data analysis in presence of three cases: complete, censored and censored with covariates.
Book ChapterDOI
Recent Achievements in Modified Chi-Squared Goodness-of-Fit Testing
TL;DR: The theory and applications of modified chi-squared tests are briefly discussed in this article, with recent achievements in the theory and application of these tests, in particular in reliability and survival analysis, are considered.
Journal ArticleDOI
Assessing Fit of the Lognormal Model for Response Times.
TL;DR: This paper focused on the lognormal model for response times, which is one of the most popular response time models in educational and psychological testing, and used it to evaluate response times.
References
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Journal ArticleDOI
Theory of Statistical Estimation
TL;DR: It has been pointed out to me that some of the statistical ideas employed in the following investigation have never received a strictly logical definition and analysis, and it is desirable to set out for criticism the manner in which the logical foundations of these ideas may be established.
Journal ArticleDOI
Tests of statistical hypotheses concerning several parameters when the number of observations is large
Journal ArticleDOI
A Test of Goodness of Fit
TL;DR: The Kolmogorov test as discussed by the authors is a distribution-free test of goodness of fit that is sensitive to discrepancies at the tails of the distribution rather than near the median.
Book
A Guide to Chi-Squared Testing
TL;DR: The Chi-Squared test of Pearson as discussed by the authors was used for a composite hypothesis and the Chi-squared test for an exponential family of distributions was used to test whether a given composite hypothesis is a composite or not.
Journal ArticleDOI
The Use of Maximum Likelihood Estimates in {\chi^2} Tests for Goodness of Fit
TL;DR: In this article, it was shown that the test statistic does not have a limiting χ2-distribution, but that it is stochastically larger than would be expected under the χ 2 theory.