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Multinomial goodness–of–fit: Large–sample tests with survey design correction and exact tests for small samples AgEcon
Jann, Ben.
I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods...
Tipo: Article Palavras-chave: Mgof; Mgofi; Multinomial; Goodness-of-fit; Chi-squared; Categorical data; Exact tests; Monte Carlo; Exhaustive enumeration; Combinatorial algorithms; Complex survey correction; Power-divergence statistic; Kolmogorov–Smirnov; Benford's law; Research Methods/ Statistical Methods.
Ano: 2008 URL: http://purl.umn.edu/122584
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Stigler's approach to recovering the distribution of first significant digits in natural data sets AgEcon
Lee, Joanne; Cho, Wendy K.; Judge, George G..
In 1881, Newcomb conjectured that the first significant digits (FSDs) of numbers in statistical tables would follow a logarithmic distribution with the digit “1” occurring most often. However, because Newcomb’s proposal was not presented with a theoretical basis, it was not given much attention. Fifty-seven years later, Benford argued for the same principle and showed it was relevant to a large range of data sets, and the logarithmic FSD distribution became known as “Benford’s Law.” In the mid-1940s, Stigler claimed Benford’s Law contained a theoretical inconsistency and supplied an alternative derivation for the distribution of FSDs. In this paper, we examine the theoretical basis of the Stigler distribution and extend his reasoning by incorporating FSD...
Tipo: Working or Discussion Paper Palavras-chave: Benford's law; Stigler's law; Power law; Maximum entropy; Distance measures; Research and Development/Tech Change/Emerging Technologies; Research Methods/ Statistical Methods; C10; C24.
Ano: 2009 URL: http://purl.umn.edu/47000
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