scholarly journals A Quasi-Score Statistic for Homogeneity Testing against Covariate-Varying Heterogeneity

2017 ◽  
Vol 45 (3) ◽  
pp. 465-481 ◽  
Author(s):  
David Todem ◽  
Wei-Wen Hsu ◽  
Jason P. Fine
Keyword(s):  
Score Statistic ◽  
Homogeneity Testing

scholarly journals Home sweet home: Quantifying home court advantages for NCAA basketball statistics

2021 ◽  
pp. 1-12
Author(s):  
Matthew van Bommel ◽  
Luke Bornn ◽  
Peter Chow-White ◽  
Chuancong Gao
Keyword(s):  
Regression Model ◽  
National Collegiate Athletic Association ◽  
Score Statistic ◽  
Present Evidence ◽  
Home Team ◽  
The Road ◽  
Score Statistics ◽  
Location Games ◽  
On The Road ◽  
Measures Of Performance

Box score statistics are the baseline measures of performance for National Collegiate Athletic Association (NCAA) basketball. Between the 2011-2012 and 2015-2016 seasons, NCAA teams performed better at home compared to on the road in nearly all box score statistics across both genders and all three divisions. Using box score data from over 100,000 games spanning the three divisions for both women and men, we examine the factors underlying this discrepancy. The prevalence of neutral location games in the NCAA provides an additional angle through which to examine the gaps in box score statistic performance, which we believe has been underutilized in existing literature. We also estimate a regression model to quantify the home court advantages for box score statistics after controlling for other factors such as number of possessions, and team strength. Additionally, we examine the biases of scorekeepers and referees. We present evidence that scorekeepers tend to have greater home team biases when observing men compared to women, higher divisions compared to lower divisions, and stronger teams compared to weaker teams. Finally, we present statistically significant results indicating referee decisions are impacted by attendance, with larger crowds resulting in greater bias in favor of the home team.

Polished homogeneity testing of Corning fused silica boules

1999 ◽  
Author(s):  
Andrew W. Fanning ◽  
Joseph F. Ellison ◽  
Daniel E. Green
Keyword(s):  
Fused Silica ◽  
Homogeneity Testing

scholarly journals Bartlett-type modification for Rao's efficient score statistic

1991 ◽  
Vol 36 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Tapas K. Chandra ◽  
Rahul Mukerjee
Keyword(s):  
Score Statistic ◽  
Efficient Score

scholarly journals Generalizing Terwilliger's likelihood approach: a new score statistic to test for genetic association

BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
pp. 63 ◽  
Author(s):  
Rachid el Galta ◽  
Shirley Uitte de Willige ◽  
Marieke CH de Visser ◽  
Quinta Helmer ◽  
Li Hsu ◽  
...  
Keyword(s):  
Genetic Association ◽  
Score Statistic ◽  
Likelihood Approach

scholarly journals Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Keyi Mou ◽  
Zhiming Li
Keyword(s):  
Binary Data ◽  
Estimation Error ◽  
Control Group ◽  
Homogeneity Test ◽  
Optimal Algorithms ◽  
Score Statistic ◽  
Correlated Binary Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Rate Score

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers

2017 ◽  
Vol 28 (4) ◽  
pp. 1019-1043 ◽  
Author(s):  
Shi-Fang Qiu ◽  
Xiao-Song Zeng ◽  
Man-Lai Tang ◽  
Wai-Yin Poon
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Test Procedure ◽  
Sample Size Determination ◽  
Double Sampling ◽  
Score Statistic ◽  
Wald Statistic ◽  
Test Procedures ◽  
Practical Applications ◽  
Logit Transformation

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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