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Journal articles on the topic 'Chi square tests'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Ingster, Yu I. "Adaptive chi-square tests." Journal of Mathematical Sciences 99, no. 2 (April 2000): 1110–19. http://dx.doi.org/10.1007/bf02673632.

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2

Nihan, Sölpük Turhan. "Karl Pearsons chi-square tests." Educational Research and Reviews 15, no. 9 (September 30, 2020): 575–80. http://dx.doi.org/10.5897/err2019.3817.

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3

Nowacki, Amy. "Chi-square and Fisher’s exact tests." Cleveland Clinic Journal of Medicine 84, no. 9 suppl 2 (September 2017): e20-e25. http://dx.doi.org/10.3949/ccjm.84.s2.04.

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4

MaCurdy, Thomas E., and Keunkwan Ryu. "Equivalence results in chi-square tests." Economics Letters 80, no. 3 (September 2003): 329–36. http://dx.doi.org/10.1016/s0165-1765(03)00124-1.

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5

Schober, Patrick, and Thomas R. Vetter. "Chi-square Tests in Medical Research." Anesthesia & Analgesia 129, no. 5 (November 2019): 1193. http://dx.doi.org/10.1213/ane.0000000000004410.

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6

Ermakov, M. S. "Asymptotic Minimaxity of Chi-Square Tests." Theory of Probability & Its Applications 42, no. 4 (January 1998): 589–610. http://dx.doi.org/10.1137/s0040585x97976441.

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7

Gagunashvili, N. D. "Chi-square tests for comparing weighted histograms." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 614, no. 2 (March 2010): 287–96. http://dx.doi.org/10.1016/j.nima.2009.12.037.

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8

Andrews, Donald W. K. "Chi-square diagnostic tests for econometric models." Journal of Econometrics 37, no. 1 (January 1988): 135–56. http://dx.doi.org/10.1016/0304-4076(88)90079-6.

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9

Greenwood, P., and M. S. Nikulin. "Application of tests of chi-square type." Journal of Soviet Mathematics 43, no. 6 (December 1988): 2776–91. http://dx.doi.org/10.1007/bf01129892.

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10

Robin, Jean-Marc, and Richard J. Smith. "TESTS OF RANK." Econometric Theory 16, no. 2 (April 2000): 151–75. http://dx.doi.org/10.1017/s0266466600162012.

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This paper considers tests for the rank of a matrix for which a root-T consistent estimator is available. However, in contrast to tests associated with the minimum chi-square and asymptotic least squares principles, the estimator's asymptotic variance matrix is not required to be either full or of known rank. Test statistics based on certain estimated characteristic roots are proposed whose limiting distributions are a weighted sum of independent chi-squared variables. These weights may be simply estimated, yielding convenient estimators for the limiting distributions of the proposed statistics. A sequential testing procedure is presented that yields a consistent estimator for the rank of a matrix. A simulation experiment is conducted comparing the characteristic root statistics advocated in this paper with statistics based on the Wald and asymptotic least squares principles.
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11

Andrews, Donald W. K. "Chi-Square Diagnostic Tests for Econometric Models: Theory." Econometrica 56, no. 6 (November 1988): 1419. http://dx.doi.org/10.2307/1913105.

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12

Oosterhoff, J. "THE CHOICE OF CELLS IN CHI–SQUARE TESTS." Statistica Neerlandica 39, no. 2 (June 1985): 115–28. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01132.x.

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13

Wilson, Jeffrey R. "Chi-Square Tests for Overdispersion with Multiparameter Estimates." Applied Statistics 38, no. 3 (1989): 441. http://dx.doi.org/10.2307/2347732.

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14

Lemeshko, B. Yu. "Chi-Square-Type Tests for Verification of Normality." Measurement Techniques 58, no. 6 (September 2015): 581–91. http://dx.doi.org/10.1007/s11018-015-0759-2.

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15

Iglesias, José A., Agapito Ledezma, Araceli Sanchis, and Gal A. Kaminka. "A plan classifier based on Chi-square distribution tests." Intelligent Data Analysis 15, no. 2 (March 11, 2011): 131–49. http://dx.doi.org/10.3233/ida-2010-0460.

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16

Makambi, Kepher. "Weighted inverse chi-square method for correlated significance tests." Journal of Applied Statistics 30, no. 2 (February 2003): 225–34. http://dx.doi.org/10.1080/0266476022000023767.

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17

Cressie, Noel, and F. C. Drost. "Asymptotics for Generalized Chi-Square Goodness-of-Fit Tests." Journal of the Royal Statistical Society. Series A (Statistics in Society) 152, no. 2 (1989): 258. http://dx.doi.org/10.2307/2982927.

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18

Vuong, Quang H., and Weiren Wang. "Minimum chi-square estimation and tests for model selection." Journal of Econometrics 56, no. 1-2 (March 1993): 141–68. http://dx.doi.org/10.1016/0304-4076(93)90104-d.

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19

Dunkl, E., O. Ludwig, and R. Lotz. "Tables of Bonferroni-limits for Simultaneous Chi-square Tests." Biometrical Journal 32, no. 5 (January 19, 2007): 555–74. http://dx.doi.org/10.1002/bimj.4710320507.

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20

Gorsuch, Richard L., and Curtis Lehmann. "Chi-square and F Ratio: Which should be used when?" Journal of Methods and Measurement in the Social Sciences 8, no. 2 (August 23, 2018): 58–71. http://dx.doi.org/10.2458/v8i2.22990.

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Approximations for Chi-square and F distributions can both be computed to provide a p-value, or probability of Type I error, to evaluate statistical significance. Although Chi-square has been used traditionally for tests of count data and nominal or categorical criterion variables (such as contingency tables) and F ratios for tests of non-nominal or continuous criterion variables (such as regression and analysis of variance), we demonstrate that either statistic can be applied in both situations. We used data simulation studies to examine when one statistic may be more accurate than the other for estimating Type I error rates across different types of analysis (count data/contingencies, dichotomous, and non-nominal) and across sample sizes (Ns) ranging from 20 to 160 (using 25,000 replications for simulating p-value derived from either Chi-squares or F-ratios). Our results showed that those derived from F ratios were generally closer to nominal Type I error rates than those derived from Chi-squares. The p-values derived from F ratios were more consistent for contingency table count data than those derived from Chi-squares. The smaller than 100 the N was, the more discrepant p-values derived from Chi-squares were from the nominal p-value. Only when the N was greater than 80 did the p-values from Chi-square tests become as accurate as those derived from F ratios in reproducing the nominal p-values. Thus, there was no evidence of any need for special treatment of dichotomous dependent variables. The most accurate and/or consistent p's were derived from F ratios. We conclude that Chi-square should be replaced generally with the F ratio as the statistic of choice and that the Chi-square test should only be taught as history.
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21

Berry, Kenneth J., and Paul W. Mielke. "Monte Carlo comparisons of the asymptotic chi-square and likelihood-ratio tests with the nonasymptotic chi-square tests for sparse r × c tables." Psychological Bulletin 103, no. 2 (1988): 256–64. http://dx.doi.org/10.1037/0033-2909.103.2.256.

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22

Kim, Joo Han. "Chi-Square Goodness-of-Fit Tests for Randomly Censored Data." Annals of Statistics 21, no. 3 (September 1993): 1621–39. http://dx.doi.org/10.1214/aos/1176349275.

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23

Habib, M. G., and D. R. Thomas. "Chi-Square Goodness-if-Fit Tests for Randomly Censored Data." Annals of Statistics 14, no. 2 (June 1986): 759–65. http://dx.doi.org/10.1214/aos/1176349953.

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24

García-Pérez, A. "Chi-Square Tests Under Models Close to the Normal Distribution." Metrika 63, no. 3 (January 20, 2006): 343–54. http://dx.doi.org/10.1007/s00184-005-0024-9.

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25

Voloshko, Valeriy A., and Egor V. Vecherko. "New upper bounds for noncentral chi-square cdf." Journal of the Belarusian State University. Mathematics and Informatics, no. 1 (March 31, 2020): 70–74. http://dx.doi.org/10.33581/2520-6508-2020-1-70-74.

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Some new upper bounds for noncentral chi-square cumulative density function are derived from the basic symmetries of the multidimensional standard Gaussian distribution: unitary invariance, components independence in both polar and Cartesian coordinate systems. The proposed new bounds have analytically simple form compared to analogues available in the literature: they are based on combination of exponents, direct and inverse trigonometric functions, including hyperbolic ones, and the cdf of the one dimensional standard Gaussian law. These new bounds may be useful both in theory and in applications: for proving inequalities related to noncentral chi-square cumulative density function, and for bounding powers of Pearson’s chi-squared tests.
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26

Berry, Kenneth J., and Paul W. Mielke. "Subroutines for Computing Exact CHI-Square and Fisher's Exact Probability Tests." Educational and Psychological Measurement 45, no. 1 (March 1985): 153–59. http://dx.doi.org/10.1177/0013164485451016.

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27

Quine, M. P., and J. Robinson. "Efficiencies of Chi-Square and Likelihood Ratio Goodness-of-Fit Tests." Annals of Statistics 13, no. 2 (June 1985): 727–42. http://dx.doi.org/10.1214/aos/1176349550.

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28

Rao, J. N. K., and A. J. Scott. "On Simple Adjustments to Chi-Square Tests with Sample Survey Data." Annals of Statistics 15, no. 1 (March 1987): 385–97. http://dx.doi.org/10.1214/aos/1176350273.

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29

Sutrick, Kenneth H. "Asymptotic power comparison of the chi-square and likelihood ratio tests." Annals of the Institute of Statistical Mathematics 38, no. 3 (December 1986): 503–11. http://dx.doi.org/10.1007/bf02482537.

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30

Winterstein, Scott R. "Chi-Square Tests for Intrabrood Independence When Using the Mayfield Method." Journal of Wildlife Management 56, no. 2 (April 1992): 398. http://dx.doi.org/10.2307/3808842.

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31

Berry, Mari, Brian Peacock, Bobbie Foote, and Lawrence Leemis. "Visual Assessment vs. Statistical Goodness of Fit Tests for Identifying Parent Population." Proceedings of the Human Factors Society Annual Meeting 32, no. 7 (October 1988): 460–64. http://dx.doi.org/10.1177/154193128803200701.

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Statistical tests are used to identify the parent distribution corresponding to a data set. A human observer looking at a histogram can also identify a probability distribution that models the parent distribution. The accuracy of a human observer was compared to the chi-square test for discrete data and the Kolmogorov-Smirnov and chi-square tests for continuous data. The human observer proved more accurate in identifying continuous distributions and the chi-square test proved to be superior in identifying discrete distributions. The effect of sample size and number of intervals in the histogram was included in the experimental design.
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32

Zhang, Qingbua, and Michèle Basseville. "Advanced numerical computation of chi-square tests for fault detection and isolation." IFAC Proceedings Volumes 36, no. 5 (June 2003): 209–14. http://dx.doi.org/10.1016/s1474-6670(17)36495-9.

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33

Gagunashvili, N. D. "Chi-square goodness of fit tests for weighted histograms. Review and improvements." Journal of Instrumentation 10, no. 05 (May 11, 2015): P05004. http://dx.doi.org/10.1088/1748-0221/10/05/p05004.

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34

Dow, Malcolm M. "Saving the Theory: On Chi-Square Tests With Cross-Cultural Survey Data." Cross-Cultural Research 27, no. 3-4 (August 1993): 247–76. http://dx.doi.org/10.1177/106939719302700305.

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35

Koehler, Kenneth J., and Jeffrey R. Wilson. "Chi–square tests for comparing vectors of proportions for several cluster samples." Communications in Statistics - Theory and Methods 15, no. 10 (January 1986): 2977–90. http://dx.doi.org/10.1080/03610928608829290.

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36

Amemiya, Yasuo, and T. W. Anderson. "Asymptotic Chi-Square Tests for a Large Class of Factor Analysis Models." Annals of Statistics 18, no. 3 (September 1990): 1453–63. http://dx.doi.org/10.1214/aos/1176347760.

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37

Wright, Bryan E. "Use of chi-square tests to analyze scat-derived diet composition data." Marine Mammal Science 26, no. 2 (April 2010): 395–401. http://dx.doi.org/10.1111/j.1748-7692.2009.00308.x.

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38

Markowski, Edward P., and Carol A. Markowski. "A Systematic Method for Teaching Post Hoc Analysis of Chi-Square Tests." Decision Sciences Journal of Innovative Education 7, no. 1 (January 2009): 59–65. http://dx.doi.org/10.1111/j.1540-4609.2008.00202.x.

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39

Quessy, Jean-François, Louis-Paul Rivest, and Marie-Hélène Toupin. "Goodness-of-fit tests for the family of multivariate chi-square copulas." Computational Statistics & Data Analysis 140 (December 2019): 21–40. http://dx.doi.org/10.1016/j.csda.2019.04.008.

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40

Brown, Mark, and Marcia H. Flicker. "Chi-Square Goodness of Fit: A Failure Rate Perspective." Probability in the Engineering and Informational Sciences 5, no. 3 (July 1991): 273–84. http://dx.doi.org/10.1017/s0269964800002084.

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Employing a failure rate approach, we propose a test statistic, , for the classic goodness-of-fit problem. It is then shown that a variation of , obtained by replacing observed variances by expected variances (an unwise change in our point of view), leads to the classical test statistic, x2. We argue that should be better approximated by a chi-square distribution, and thus employing , the true p−values should be closer to the nominal p−values than under x2 Various other comparisons of the two tests are made.
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41

Hosmane, B. S. "Improved likelihood ratio tests and pearson chi-square tests for independence in two dimensional contingency tables." Communications in Statistics - Theory and Methods 15, no. 6 (January 1986): 1875–88. http://dx.doi.org/10.1080/03610928608829224.

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42

York, Kenneth M. "Disparate Results in Adverse Impact Tests: The 4/5ths Rule and the Chi Square Test." Public Personnel Management 31, no. 2 (June 2002): 253–62. http://dx.doi.org/10.1177/009102600203100210.

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The Uniform Guidelines on Employee Selection Procedures suggests the “4/5ths rule” for testing the outcome of selection procedures for adverse impact. The appropriate statistical test is a Chi Square testing for a significant difference between the selection ratios of the minority and majority applicants. However, the 4/5ths rule and Chi Square test do not agree 10–40% of the time depending on sample size, making it difficult for personnel managers to monitor their organization's Equal Employment Opportunity compliance. The extent of disagreement between the two tests is described, and recommendations are given for how personnel managers should use the 4/5ths rule and the Chi Square test to determine whether a selection procedure has adverse impact.
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43

Jolliffe, Ian T., and Cristina Primo. "Evaluating Rank Histograms Using Decompositions of the Chi-Square Test Statistic." Monthly Weather Review 136, no. 6 (June 1, 2008): 2133–39. http://dx.doi.org/10.1175/2007mwr2219.1.

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Abstract Rank histograms are often plotted to evaluate the forecasts produced by an ensemble forecasting system—an ideal rank histogram is “flat” or uniform. It has been noted previously that the obvious test of “flatness,” the well-known χ2 goodness-of-fit test, spreads its power thinly and hence is not good at detecting specific alternatives to flatness, such as bias or over- or underdispersion. Members of the Cramér–von Mises family of tests do much better in this respect. An alternative to using the Cramér–von Mises family is to decompose the χ2 test statistic into components that correspond to specific alternatives. This approach is described in the present paper. It is arguably easier to use and more flexible than the Cramér–von Mises family of tests, and does at least as well as it in detecting alternatives corresponding to bias and over- or underdispersion.
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44

Mirvaliev, M. "Chi-Square Goodness-of-Fit Tests for a Family of Multidimensional Discrete Distributions." Theory of Probability & Its Applications 34, no. 4 (January 1990): 728–32. http://dx.doi.org/10.1137/1134094.

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45

Yuan, Ke-Hai, and Wai Chan. "Measurement invariance via multigroup SEM: Issues and solutions with chi-square-difference tests." Psychological Methods 21, no. 3 (September 2016): 405–26. http://dx.doi.org/10.1037/met0000080.

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46

Suh, Youngsuk, and Sun-Joo Cho. "Chi-Square Difference Tests for Detecting Differential Functioning in a Multidimensional IRT Model." Applied Psychological Measurement 38, no. 5 (February 26, 2014): 359–75. http://dx.doi.org/10.1177/0146621614523116.

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47

Anderson, T. W. "Goodness-of-Fit Tests for Probability Distributions and Spectral Distributions." Probability in the Engineering and Informational Sciences 9, no. 1 (January 1995): 27–37. http://dx.doi.org/10.1017/s0269964800003661.

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In the fall of 1948 in my course on Least Squares in the Department of Mathematical Statistics at Columbia University (and in the spring in Correlation and Chi-Square), I was particularly impressed by one of the students— Gerald J. Lieberman. I was disappointed that this promising student left Columbia after one year, but it was not long until our paths met again. It is a pleasure to dedicate this paper to my colleague and close friend!
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48

Jelinski, Dennis E. "On the use of chi-square analyses in studies of resource utilization." Canadian Journal of Forest Research 21, no. 1 (January 1, 1991): 58–65. http://dx.doi.org/10.1139/x91-009.

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Chi-square (χ2) tests are analytic procedures that are often used to test the hypothesis that animals use a particular food item or habitat in proportion to its availability. Unfortunately, several sources of error are common to the use of χ2 analysis in studies of resource utilization. Both the goodness-of-fit and homogeneity tests have been incorrectly used interchangeably when resource availabilities are estimated or known apriori. An empirical comparison of the two methods demonstrates that the χ2 test of homogeneity may generate results contrary to the χ2 goodness-of-fit test. Failure to recognize the conservative nature of the χ2 homogeneity test, when "expected" values are known apriori, may lead to erroneous conclusions owing to the increased possibility of committing a type II error. Conversely, proper use of the goodness-of-fit method is predicated on the availability of accurate maps of resource abundance, or on estimates of resource availability based on very large sample sizes. Where resource availabilities have been estimated from small sample sizes, the use of the χ2 goodness-of-fit test may lead to type I errors beyond the nominal level of α. Both tests require adherence to specific critical assumptions that often have been violated, and accordingly, these assumptions are reviewed here. Alternatives to the Pearson χ2 statistic are also discussed.
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49

Zeis, Charles, Hailu Regassa, Abhay Shah, and Ahmad Ahmadian. "Goodness-of-fit tests for rating scale data: Applying the minimum chi-square method." Journal of Economic and Social Measurement 27, no. 1-2 (April 1, 2001): 25–39. http://dx.doi.org/10.3233/jem-2001-0193.

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50

Ross, Donald C. "Chi Square Tests for the Difference between Correlated Weighted Kappas and Correlated Unweighted Kappas'." Educational and Psychological Measurement 52, no. 2 (June 1992): 293–300. http://dx.doi.org/10.1177/0013164492052002004.

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