Pearson-Fisher Chi-Square Statistic Revisited
نویسندگان
چکیده
منابع مشابه
Pearson-Fisher Chi-Square Statistic Revisited
The Chi-Square test (χ 2 test) is a family of tests based on a series of assumptions and is frequently used in the statistical analysis of experimental data. The aim of our paper was to present solutions to common problems when applying the Chi-square tests for testing goodness-of-fit, homogeneity and independence. The main characteristics of these three tests are presented along with various p...
متن کاملComponents of the Pearson-Fisher Chi-squared Statistic
The Pearson-Fisher chi-squared test can be used to evaluate the goodnessof-fit of categorized continuous data with known bin endpoints compared to a continuous distribution, in the presence of unknown (nuisance) distribution parameters. Rayner and McAlevey [11] and Rayner and Best [9],[10] demonstrate that in this case, component tests of the Pearson-Fisher chi-squared test statistic can be obt...
متن کاملComponents of the Pearson-Fisher chi-squared statistic
The Pearson-Fisher chi-squared test can be used to evaluate the goodnessof-fit of categorized continuous data with known bin endpoints compared to a continuous distribution, in the presence of unknown (nuisance) distribution parameters. Rayner and McAlevey [11] and Rayner and Best [9],[10] demonstrate that in this case, component tests of the Pearson-Fisher chi-squared test statistic can be obt...
متن کاملOn Empirical Likelihood and Non-parametric Pearson's Chi-square Statistic
Owen (1988) showed that the non-parametric empirical likelihood ratio under a linear constrain has an asymptotic chi-square distribution, same as in the paramet-ric case. This fact can be used to form (non-parametric, asymptotic) conndence intervals. We suggest here to use the non-parametric version of the Pearson's chi-square statistic instead of the likelihood ratio to form conndence interval...
متن کاملStandardizing the Empirical Distribution Function Yields the Chi-Square Statistic
Standardizing the empirical distribution function yields a statistic with norm square that matches the chi-square test statistic. To show this one may use the covariance matrix of the empirical distribution which, at any finite set of points, is shown to have an inverse which is tridiagonal. Moreover, a representation of the inverse is given which is a product of bidiagonal matrices correspondi...
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ژورنال
عنوان ژورنال: Information
سال: 2011
ISSN: 2078-2489
DOI: 10.3390/info2030528