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Journal articles on the topic 'Statistic and probability'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Driscoll, Michale F., and W. D. Laigh. "Characterizations and o-statistic representations of lu-statistics." Communications in Statistics - Theory and Methods 17, no. 8 (1988): 2763–77. http://dx.doi.org/10.1080/03610928808829769.

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2

Long, Michael A., Kenneth J. Berry, and Paul W. Mielke. "Multiway Contingency Tables: Monte Carlo Resampling Probability Values for the Chi-Squared and Likelihood-Ratio Tests." Psychological Reports 107, no. 2 (2010): 501–10. http://dx.doi.org/10.2466/03.pr0.107.5.501-510.

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Monte Carlo resampling methods to obtain probability values for chi-squared and likelihood-ratio test statistics for multiway contingency tables are presented. A resampling algorithm provides random arrangements of cell frequencies in a multiway contingency table, given fixed marginal frequency totals. Probability values are obtained from the proportion of resampled test statistic values equal to or greater than the observed test statistic value.

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3

Kudlaev, E. M. "Separable statistics derived from a given separable statistic by “restricting” the probability space." Journal of Mathematical Sciences 81, no. 4 (1996): 2824–29. http://dx.doi.org/10.1007/bf02362487.

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4

Jäntschi, Lorentz, and Sorana D. Bolboacă. "Computation of Probability Associated with Anderson–Darling Statistic." Mathematics 6, no. 6 (2018): 88. http://dx.doi.org/10.3390/math6060088.

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5

Oni Yuliani, Oni. "The Teaching Statistic Using Matlab." KURVATEK 4, no. 2 (2019): 1–10. http://dx.doi.org/10.33579/krvtk.v4i2.1193.

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This paper is to describe the use of Matlab as a scientific tool for the teaching of statistics in undergraduate school. Application of this software in the teaching of some difficult topics like probability concepts, probability distribution, statistical significance, and significance tests, were demonstrated using the Matlab. Matlab has proved itself to be a very effective tool in the educational process because it offers a simple and powerful tool for analyzing and visualizing results of numerical simulations and measurements.

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6

Pacini, David. "Proximal statistic: Asymptotic normality." Statistics & Probability Letters 167 (December 2020): 108896. http://dx.doi.org/10.1016/j.spl.2020.108896.

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7

LaBudde, Robert A., and James Harnly. "Probability of Identification: A Statistical Model for the Validation of Qualitative Botanical Identification Methods." Journal of AOAC INTERNATIONAL 95, no. 1 (2012): 273–85. http://dx.doi.org/10.5740/jaoacint.11-266.

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Abstract A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

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8

CHEN, ZHENMIN, and CHUNMIAO YE. "AN ALTERNATIVE TEST FOR UNIFORMITY." International Journal of Reliability, Quality and Safety Engineering 16, no. 04 (2009): 343–56. http://dx.doi.org/10.1142/s0218539309003435.

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Improving power of goodness-of-fit tests is an important research topic in statistics. The goal of the goodness-of-fit test is to check whether the underlying probability distribution, from which a sample is drawn, differs from a hypothesized distribution. Numerous research papers have been published in this area. It has been shown that the power of the existing goodness-of-fit tests in the literature is unsatisfactory when the alternative distributions are of V-shape or when the sample sizes are small. This motivates the development of more powerful test statistics. In this research, a new test statistic is proposed. The result can be used to test whether the underlying probability distribution differs from a uniform distribution. By applying the probability integral transformation, the proposed test statistic can be used to check whether the underlying distribution differs from any hypothesized distribution. The performance of the method proposed in this research is compared with the Kolmogorov–Smirnov test, which is a widely adopted statistical test in the literature. It has been shown that the test proposed in this proposal is more powerful than the Kolmogorov–Smirnov test in some cases.

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9

Gladkiy, E. G. "Jacobi probability distribution for approximation of emperic statistic distributions." Technical mechanics 2017, no. 1 (2017): 107–22. http://dx.doi.org/10.15407/itm2017.01.107.

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10

Hassainia, Farid, Dominique Petit, and Jacques Montplaisir. "Significance probability mapping: The final touch int-statistic mapping." Brain Topography 7, no. 1 (1994): 3–8. http://dx.doi.org/10.1007/bf01184832.

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11

Johnston, Janis E., Kenneth J. Berry, and Paul W. Mielke. "Resampling Permutation Probability Values for Six Measures of Qualitative Variation." Perceptual and Motor Skills 104, no. 3 (2007): 773–76. http://dx.doi.org/10.2466/pms.104.3.773-776.

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An algorithm and associated FORTRAN program are provided for six common measures of ordinal association: Kendall's τ a and τ b, Stuart's τ c, Goodman and Kruskal's γ, and Somers' dyx and dxy. Program ROMA reports the observed data table, the values for the six test statistics, and the resampling upper- and lower-tail probability values associated with each test statistic.

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12

Moghimi, Maryam, and Herbert W. Corley. "Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions." Data 5, no. 3 (2020): 84. http://dx.doi.org/10.3390/data5030084.

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In this paper, we study the information lost when a real-valued statistic is used to reduce or summarize sample data from a discrete random variable with a one-dimensional parameter. We compare the probability that a random sample gives a particular data set to the probability of the statistic’s value for this data set. We focus on sufficient statistics for the parameter of interest and develop a general formula independent of the parameter for the Shannon information lost when a data sample is reduced to such a summary statistic. We also develop a measure of entropy for this lost information that depends only on the real-valued statistic but neither the parameter nor the data. Our approach would also work for non-sufficient statistics, but the lost information and associated entropy would involve the parameter. The method is applied to three well-known discrete distributions to illustrate its implementation.

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13

Hines, R. J. O'Hara, and W. G. S. Hines. "Exploring Cook's Statistic Graphically." American Statistician 49, no. 4 (1995): 389. http://dx.doi.org/10.2307/2684582.

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14

Surgailis, Donatas, Gilles Teyssière, and Marijus Vaičiulis. "The increment ratio statistic." Journal of Multivariate Analysis 99, no. 3 (2008): 510–41. http://dx.doi.org/10.1016/j.jmva.2007.01.014.

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15

Chistyakov, G. P., and F. Gotze. "Moderate Deviations for Student's Statistic." Theory of Probability & Its Applications 47, no. 3 (2003): 415–28. http://dx.doi.org/10.1137/s0040585x97979846.

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16

Hawkins, D. L. "Using U Statistics to Derive the Asymptotic Distribution of Fisher's Z Statistic." American Statistician 43, no. 4 (1989): 235. http://dx.doi.org/10.2307/2685369.

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17

Agundu, Prince Umor C., and Waleru Henry Akani. "Dynamism of Monetary Transmission Mechanism in Nigeria: Interest Rate and Market Capitalization Causality Evidence." International Journal of Accounting & Finance Review 2, no. 1 (2018): 81–91. http://dx.doi.org/10.46281/ijafr.v2i1.24.

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The potency of monetary transmission channels anchors the process by which interest rate movements and other cardinal aggregates influence critical financial fundamentals in an economy. This study, thus, examines dynamism of the monetary transmission mechanism with focus on the causality of interest rate and market capitalization in the Nigerian economy. Time series data covering a period of 36 years (1981 - 2015) were extracted from publications of monetary authorities and related agencies, including annual reports of Deposit Money Banks (DMBs) in the country. Facilitated by E-Views software, the analytical proceedings generated the required statistical outcomes in terms of coefficient of correlation (r), coefficient of determination (R2), t-statistic, and F-statistic. Granger causality test was also conducted to clearly establish the direction of causality between the focal variables. Essentially, the null hypothesis is rejected as probability of the F-statistic is less than the specified 0.05 level of significance. The granger causality test statistics run from four interest rate components to the operational capital market fundamental (with F-statistics of 5.758, 5.540, 4.209,and5.656; as well as probability values of 0.008, 0.009, 0.002, and 0.009 respectively). In view of the analytical outcomes, it is recommended that interest rate components be efficiently synergized to boost investors’ confidence and further drive monetary policy dynamics towards greater financial system vitality and sustainability in Nigeria.

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18

Kawasaki, Tamae, and Takashi Seo. "Modified Likelihood Ratio Test for Sub-mean Vectors with Two-step Monotone Missing Data in Two-sample Problem." Austrian Journal of Statistics 50, no. 1 (2021): 88–104. http://dx.doi.org/10.17713/ajs.v50i1.928.

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This article deals with the problem of testing for two normal sub-mean vectors when the data set have two-step monotone missing observations. Under the assumptions that the population covariance matrices are equal, we obtain the likelihood ratio test (LRT) statistic. Furthermore, an asymptotic expansion for the null distribution of the LRT statistic is derived under the two-step monotone missing data by the perturbation method. Using the result, we propose two improved statistics with good chi-squared approximation. One is the modified LRT statistic by Bartlett correction,and the other is the modified LRT statistic using the modification coefficient by linear interpolation. The accuracy of the approximations are investigated by using a Monte Carlo simulation. The proposed methods are illustrated using an example.

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19

Yam, Jim Hoy. "AMBIGUITAS STATISTIKA DESKRIPTIF & STATISTIKA INFERENSIAL." JURNAL PENELITIAN DAN KARYA ILMIAH 20, no. 2 (2020): 117–24. http://dx.doi.org/10.33592/pelita.vol20.iss2.664.

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There is ambiguity of descriptive statistics and inferential statistics, which are applied on a scientific research paper. First ambiguity: Is it appropriate to apply 2 types of statistic in a research? And the second ambiguity: whether inferential statistics can utilize non-probability sample data? Those 2 questions motivate me to write this paper. The aim are to find answers of the ambiguity; by based on research behavior phenomenon, which are referring to research theory and research theorem. At the eventually, the conclusion, descriptive statistics and inferential statistics are need to be joined in a study to shake of the research output in terms to describing data of population or sample, then followed by data testing and data analyzing to draw conclusions. While the next ambiguity concerning of using non probability sampling by inferential statistic; it have been done by some researchers, with the excuse of result achievement closer to actual condition.

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20

Pigeon, Joseph G., and Joseph F. Heyse. "An Improved Goodness of Fit Statistic for Probability Prediction Models." Biometrical Journal 41, no. 1 (1999): 71–82. http://dx.doi.org/10.1002/(sici)1521-4036(199903)41:1<71::aid-bimj71>3.0.co;2-o.

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21

Tanaka, Katsuto. "Asymptotic expansions for time series statistics." Journal of Applied Probability 23, A (1986): 211–27. http://dx.doi.org/10.2307/3214354.

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Asymptotic expansions for the distributions of estimators and test statistics are derived in connection with time series models. The expansions relate to marginal and joint distributions together with the percentiles of marginal distributions. We also consider transforming a statistic so that the transformed statistic has a distribution that coincides with its asymptotic distribution up to a higher order.

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22

Berry, Kenneth J., Janis E. Johnston, and Paul W. Mielke. "Exact Permutation Probability Values for Weighted Kappa." Psychological Reports 102, no. 1 (2008): 53–57. http://dx.doi.org/10.2466/pr0.102.1.53-57.

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23

Johnston, Janis E., Kenneth J. Berry, and Paul W. Mielke. "Resampling Permutation Probability Values for Weighted Kappa." Psychological Reports 103, no. 2 (2008): 467–75. http://dx.doi.org/10.2466/pr0.103.2.467-475.

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A permutation algorithm and associated FORTRAN program are provided for resampling weighted kappa. Program RWK provides the weighted kappa test statistic and the resampling one-sided upper-tail probability value.

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24

Gribkova, Nadezhda. "Cramér type large deviations for trimmed L-statistics." Probability and Mathematical Statistics 37, no. 1 (2018): 101–18. http://dx.doi.org/10.19195/0208-4147.37.1.4.

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CRAMÉR TYPE LARGE DEVIATIONS FOR TRIMMED L-STATISTICSIn this paper, we propose a new approach to the investigationof asymptotic properties of trimmed L-statistics and we apply it to the Cramér type large deviation problem. Our results can be compared with those in Callaert et al. 1982 – the first and, as far as we know, the single article where some results on probabilities of large deviations for the trimmed L-statistics were obtained, but under some strict and unnatural conditions. Our approach is to approximate the trimmed L-statistic by a non-trimmed L-statistic with smooth weight function based onWinsorized random variables. Using this method, we establish the Cramér type large deviation results for the trimmed L-statistics under quite mild and natural conditions.

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25

Bargagliotti, Anna E., and Raymond N. Greenwell. "Combinatorics and Statistical Issues Related to the Kruskal–Wallis Statistic." Communications in Statistics - Simulation and Computation 44, no. 2 (2014): 533–50. http://dx.doi.org/10.1080/03610918.2013.786781.

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26

Dinov, Ivo D., Juana Sanchez, and Nicolas Christou. "Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses." Computers & Education 50, no. 1 (2008): 284–300. http://dx.doi.org/10.1016/j.compedu.2006.06.003.

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27

Gillard, Jonathan, and Anatoly Zhigljavsky. "Optimal directional statistic for general regression." Statistics & Probability Letters 143 (December 2018): 74–80. http://dx.doi.org/10.1016/j.spl.2018.07.025.

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28

Xie, Jingdong, and Carey E. Priebe. "Generalizing the mann-whitney-wilcoxon statistic." Journal of Nonparametric Statistics 12, no. 5 (2000): 661–82. http://dx.doi.org/10.1080/10485250008832827.

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29

Stute, Winfried. "$U$-Statistic Processes: A Martingale Approach." Annals of Probability 22, no. 4 (1994): 1725–44. http://dx.doi.org/10.1214/aop/1176988480.

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30

Does, R. J. M. M., R. Helmers, and C. A. J. Klaassen. "Approximating the distribution of Greenwood's statistic." Statistica Neerlandica 42, no. 3 (1988): 153–62. http://dx.doi.org/10.1111/j.1467-9574.1988.tb01230.x.

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31

del Río, Manuel. "Cook statistic and diagnostic for transformations." Statistics & Probability Letters 6, no. 4 (1988): 281–86. http://dx.doi.org/10.1016/0167-7152(88)90075-2.

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32

Yang, Zhenlin. "A new statistic for regression transformation." Test 9, no. 1 (2000): 123–31. http://dx.doi.org/10.1007/bf02595854.

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33

Somes, Grant W. "The Generalized Mantel-Haenszel Statistic." American Statistician 40, no. 2 (1986): 106. http://dx.doi.org/10.2307/2684866.

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34

Siino, Marianna, Salvatore Fasola, and Vito MR Muggeo. "Inferential tools in penalized logistic regression for small and sparse data: A comparative study." Statistical Methods in Medical Research 27, no. 5 (2016): 1365–75. http://dx.doi.org/10.1177/0962280216661213.

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This paper focuses on inferential tools in the logistic regression model fitted by the Firth penalized likelihood. In this context, the Likelihood Ratio statistic is often reported to be the preferred choice as compared to the ‘traditional’ Wald statistic. In this work, we consider and discuss a wider range of test statistics, including the robust Wald, the Score, and the recently proposed Gradient statistic. We compare all these asymptotically equivalent statistics in terms of interval estimation and hypothesis testing via simulation experiments and analyses of two real datasets. We find out that the Likelihood Ratio statistic does not appear the best inferential device in the Firth penalized logistic regression.

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35

Rodríguez, Juan C., and Aldo J. Viollaz. "A weighted cramer-von mises statistic derived from a decomposition of the anderson-darling statistic." Communications in Statistics - Theory and Methods 28, no. 10 (1999): 2333–46. http://dx.doi.org/10.1080/03610929908832424.

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36

Zhang, Zhenkui, Renato Assunção, and Martin Kulldorff. "Spatial Scan Statistics Adjusted for Multiple Clusters." Journal of Probability and Statistics 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/642379.

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The spatial scan statistic is one of the main epidemiological tools to test for the presence of disease clusters in a geographical region. While the statistical significance of the most likely cluster is correctly assessed using the model assumptions, secondary clusters tend to have conservatively highP-values. In this paper, we propose a sequential version of the spatial scan statistic to adjust for the presence of other clusters in the study region. The procedure removes the effect due to the more likely clusters on less significant clusters by sequential deletion of the previously detected clusters. Using the Northeastern United States geography and population in a simulation study, we calculated the type I error probability and the power of this sequential test under different alternative models concerning the locations and sizes of the true clusters. The results show that the type I error probability of our method is close to the nominalαlevel and that for secondary clusters its power is higher than the standard unadjusted scan statistic.

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37

Kulldorff, Martin, Lan Huang, Linda Pickle, and Luiz Duczmal. "An elliptic spatial scan statistic." Statistics in Medicine 25, no. 22 (2006): 3929–43. http://dx.doi.org/10.1002/sim.2490.

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38

Fu, James C., Tung-Lung Wu, and W. Y. Wendy Lou. "Continuous, Discrete, and Conditional Scan Statistics." Journal of Applied Probability 49, no. 01 (2012): 199–209. http://dx.doi.org/10.1017/s0021900200008949.

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The distributions for continuous, discrete, and conditional discrete scan statistics are studied. The approach of finite Markov chain imbedding, which has been applied to random permutations as well as to runs and patterns, is extended to compute the distribution of the conditional discrete scan statistic, defined from a sequence of Bernoulli trials. It is shown that the distribution of the continuous scan statistic induced by a Poisson process defined on (0, 1] is a limiting distribution of weighted distributions of conditional discrete scan statistics. Comparisons of rates of convergence as well as numerical comparisons of various bounds and approximations are provided to illustrate the theoretical results.

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39

Jodra, Pedro. "ON ORDER STATISTICS FROM THE GOMPERTZ–MAKEHAM DISTRIBUTION AND THE LAMBERT W FUNCTION." Mathematical Modelling and Analysis 18, no. 3 (2013): 432–45. http://dx.doi.org/10.3846/13926292.2013.807316.

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The aim of this paper is twofold. First, we show that the expected value of the minimum order statistic from the Gompertz-Makeham distribution can be expressed in closed form in terms of the incomplete gamma function. We also give a general formula for the moments of the minimum order statistic in terms of the generalized integro-exponential function. As a consequence, the moments of all order statistics from this probability distribution can be more easily evaluated from the moments of the minimum order statistic. Second, we show that the maximum and minimum order statistics from the Gompertz-Makeham distribution are in the domains of attraction of the Gumbel and Weibull distributions, respectively. Lambert W function plays an important role in solving these problems.

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40

Qiu, Shi-Fang, Xiao-Song Zeng, Man-Lai Tang, and Wai-Yin Poon. "Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers." Statistical Methods in Medical Research 28, no. 4 (2017): 1019–43. http://dx.doi.org/10.1177/0962280217744239.

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Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example.

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41

Priebe, Carey E., and Lenore J. Cowen. "A generalized wilcoxon-mann-whitney statistic." Communications in Statistics - Theory and Methods 28, no. 12 (1999): 2871–78. http://dx.doi.org/10.1080/03610929908832454.

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42

Zhao, Dong Kai, Jing Yu Su, Wei Wang, and Dong Hui Ma. "Prediction of Seismic Casualties Based on Urban Land Types." Applied Mechanics and Materials 166-169 (May 2012): 2074–77. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.2074.

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A probability model based on urban land types and probability reliability was put forward to predict seismic casualties. The casualty probabilities of different urban land types were got by classifying the urban land types, combining with the historical earthquake casualty statistics and statistic residence status in the architecture of different urban land types, and then the upper value of earthquake casualties in certain probability might be estimated. Meanwhile, an example was analyzed with the proposed method. Results show the feasibility of the proposed method.

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43

Yang, James J., Elisa M. Trucco, and Anne Buu. "A hybrid method of the sequential Monte Carlo and the Edgeworth expansion for computation of very small p-values in permutation tests." Statistical Methods in Medical Research 28, no. 10-11 (2018): 2937–51. http://dx.doi.org/10.1177/0962280218791918.

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Permutation tests are very useful when parametric assumptions are violated or distributions of test statistics are mathematically intractable. The major advantage of permutation tests is that the procedure is so general that it is applicable to most test statistics. The computational expense is, however, impractical in high-dimensional settings such as genomewide association studies. This study provides a comprehensive review of existing methods that can compute very small p-values efficiently. A common issue with existing methods is that they can only be applied to a specific test statistic. To fill in the knowledge gap, we propose a hybrid method of the sequential Monte Carlo and the Edgeworth expansion approximation for a studentized statistic, which is applicable to a variety of test statistics. The simulation results show that the proposed method performs better than competing methods. Furthermore, applications of the proposed method are demonstrated by statistical analysis on the genomewide association studies data from the Study of Addiction: Genetics and Environment (SAGE).

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44

Puspitasari, Dhea Pranatania, and Sri Murdiati. "Pengaruh Pajak Tangguhan, Perencanaan Pajak dan Asset Perusahaan Terhadap Manajemen Laba Pada Perusahaan Kimia yang Terdaftar di Bursa Efek Indonesia Tahun 2013-2017." Permana : Jurnal Perpajakan, Manajemen, dan Akuntansi 10, no. 2 (2018): 186–201. http://dx.doi.org/10.24905/permana.v10i2.81.

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Objectives to be achieved in this research are: 1). To know the effect of tax deferred, tax planning and corporate assets together to earnings management at chemical company, 2). To determine the effect of deferred tax on earnings management at chemical companies, 3). To know the effect of tax planning on earnings management at chemical company, 4). To know the influence of company's asset to earnings management at chemical company. Data collection method used in this research is dentasiasi. While the data analysis and hypothesis test used are classical assumption testing, multiple linear regression analysis, simultaneous significance test (F statistic test), significant test of individual parameter (t test statistic), and Coefficient of Determination. Based on the calculation result there is significant influence of tax deferred, tax planning and company asset collectively to earnings management at chemical company proven from result of test of significant test of simultan parameter (statistic test F) got probability sig value equal to 0,022 <0,05. There is no significant partial tax effect on profit management in chemical companies as evidenced by the result of testing the significance of individual parameters (statistical test t) of deferred tax on earnings management obtained probability sig value of 0.904> 0.05. There is significant influence of tax planning partially to earnings management at chemical company proven from result of test of significant test of individual parameter (statistic test t) tax planning to earnings management got probability sig value equal to 0,013 <0,05. There is no significant influence of company asset partially to earnings management at chemical company proved from result of calculation of test of significant individual parameter (statistic test t) company asset to earnings management got probability sig value equal to 0,166 <0,05.

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45

Berry, Kenneth J., and Paul W. Mielke. "Nonasymptotic Probability Values for Cochran's Q Statistic: A Fortran 77 Program." Perceptual and Motor Skills 82, no. 1 (1996): 303–6. http://dx.doi.org/10.2466/pms.1996.82.1.303.

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A nonasymptotic inference procedure for Cochran's Q test for the equality of matched proportions is described. An algorithm and FORTRAN 77 program are provided to compute Cochran's Q test statistic and the associated nonasymptotic probability value. The nonasymptotic method provides improvement over the usual asymptotic chi-squared analysis procedure whenever the effective number of subjects is small or the number of successes is small.

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46

Jun, Sung-Hae. "Improvement of Self Organizing Maps using Gap Statistic and Probability Distribution." International Journal of Fuzzy Logic and Intelligent Systems 8, no. 2 (2008): 116–20. http://dx.doi.org/10.5391/ijfis.2008.8.2.116.

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47

黄, 洵. "Galaxy’s Approximate Luminosity Probability and Linear Statistic Analysis of Luminosity Graph." Astronomy and Astrophysics 06, no. 02 (2018): 29–46. http://dx.doi.org/10.12677/aas.2018.62003.

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Maderková, Lucia, Jaroslav Antal, and Ján Čimo. "Statistic and probability characteristics of rain factor R in Slovak Republic." EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) 3, no. 4 (2014): 254. http://dx.doi.org/10.18393/ejss.15688.

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Lim, Wooi K., and Alice W. Lim. "A Comparison Of Usual t-Test Statistic and Modified t-Test Statistics on Skewed Distribution Functions." Journal of Modern Applied Statistical Methods 15, no. 2 (2016): 67–89. http://dx.doi.org/10.22237/jmasm/1478001960.

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Zhuo, Bin, Duo Jiang, and Yanming Di. "Test-statistic correlation and data-row correlation." Statistics & Probability Letters 167 (December 2020): 108903. http://dx.doi.org/10.1016/j.spl.2020.108903.

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