On the Criterion that a Given System of Deviations from the Probable in the Case of a Correlated System of Variables is Such that it Can be Reasonably Supposed to have Arisen from Random Sampling

By K. Pearson. 1900

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Relative Age Effect and chi-squared statistics (2015, methodology): “Traditionally, the existence of this effect is determined by testing whether there is a statistically significant difference between the theoretical expected distribution of athletes’ dates of birth per quarter and the observed distribution using a chi-squared goodness-of-fit test (Pearson, 1900).”
Regularizing Generative Adversarial Networks under Limited Data (2021, methodology): “Similarly, the LSGAN [47] method leads to minimizing the χ 2 - divergence [56] and the EBGAN [81] scheme minimizes the total variation distance [2].”
Your Chi-Square Test Is Statistically Significant: Now What?. (2015, unknown): “1 The focus here is on Pearson’s (1900) chi-square tests --- not the other uses of the chi-square distribution, for example in logistic regression or structural equation modeling.”
Three centuries of categorical data analysis: Log-linear models and maximum likelihood estimation (2007, methodology): “Yet Pearson preferred to view contingency tables involving the cross-classification of two or more polytomies as arising from a partition of a set of multivariate, normal data, with an underlying continuum for each polytomy. This view led Pearson (1904) to develop his tetrachoric correlation coeffic”
Tests of Spatial Randomness Adjusted for an Inhomogeneity (2006, background): “…te st s C lu st er de te ct io n te st s Fo cu se d te st s S pa tia lly un re la te d lo ca tio ns S pa tia lly re la te d lo ca tio ns Chi-square(Pearson1900) Troup–Maynard(1912) Poissondispersionindex(Fisher1922) Renkonen(1938) Jahnetal.’sindex1(1947) Jahnetal.’sindex2(1947)…”
Simple Correspondence Analysis: A Bibliographic Review (2004, methodology): “Pearson (1900) developed the ground work for the chi-squared statistic which is used to compare the observed counts with what is expected under the hypothesis of independence between the two variables.”
AN OPTIMALITY THEORY FOR MID p-VALUES IN 2 × 2 CONTINGENCY TABLES (2001, background): “The two–sided version of the normal p–value (1.3) is the chi–squared p-value proposed by Pearson (1900): P (Z2 > t2), (1.8) where Z and t are defined below (1.3).”
Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (2017, methodology): “e can also consider testing for unconditional independence X??YjZ= z;8zwhen Zis discrete and P(Z= z) >0. The chi-squared test for instance utilizes this strategy when both Xand Yare also discrete (Pearson, 1900). Another permutation-based test generalizes the same strategy even when Xand Yare not”
TSEBRA: transcript selector for BRAKER (2021, methodology): “We found that nearly all differences in Sn and Sp are highly significant between TSEBRA and the other methods (see Additional file 1: Table S5), using McNemar’s [35] test and the chi-square test of homogeneity [36] The number of transcripts per gene selected by TSEBRA was on average 1.”
Impact of epidermal growth factor receptor expression on survival and pattern of relapse in patients with advanced head and neck carcinoma. (2002, methodology): “Comparisons of baseline characteristics were done using the Pearson 2 test (9), with the exception of age, which was performed with a t test.”
Inference Functions and Quadratic Score Tests (2003, methodology): “If we use the model-based covariance matrix in (9), we have already noted that the quadratic inference function Q2 equals the partitioned chi-squared distance, with the Pearson (1900) denominators: Q2P(θ) = ∑ [ni − nPθ(Ai)]2 nPθ(Ai) .”
The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis (2002, methodology): “Pearson (1902) then ignored the integral terms arguing that they involve the small factor f(y)− f̄(y), and the remainder term R, which by “hypothesis” is small for large enough sample size.”