Results 151 to 160 of about 400,578 (220)

Estimating and Fitting the Non-continuous category scored Polytomous Items under the Weighted Score Logistic Model and its Simulation Study. [PDF]

Appl Psychol Meas
Jian X, Dai B, Qing Y, Deng Y.
europepmc   +1 more source

Development, validity, and reliability of the band and loop radiographic assessment scoring system (BRASS). [PDF]

Eur Oral Res
Chari D, Panda A, Shukla B.
europepmc   +1 more source

Willing, but unequally: Indigenous identity influences participation in Alzheimer's disease biomarker research in Chile. [PDF]

Alzheimers Dement (Amst)
Aravena JM, Saguez R, Sandoval MH, Herrera C, Albala C, Fuentes P.   +5 more
europepmc   +1 more source

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A new estimator of Kullback–Leibler information based on a local linear regression and its application in goodness-of-fit tests

Journal of Statistical Computation and Simulation, 2023
We propose here a general statistic for the goodness-of-fit test of statistical models. The proposed statistic is constructed based on an estimate of Kullback–Leibler information.
H. Alizadeh Noughabi, M. Shafaei Noughabi   +1 more
semanticscholar   +1 more source

gofCopula: Goodness-of-Fit Tests for Copulae

The R Journal, 2021
Last decades show an increased interest in modeling various types of data through copulae. Different copula models have been developed, which lead to the challenge of finding the best fitting model for a particular dataset.
O. Okhrin, Simon Trimborn, Martin Waltz
semanticscholar   +1 more source

Improved Goodness-Of-Fit Tests

Biometrika, 1971
Two statistics for testing goodness of fit for small sample sizes are provided. The first statistic, S, can be used to test the fit to any completely specified continuous distribution function and is more powerful than the Kolmogorov-Smirnov statistic in the cases tested.
Finkelstein, J. M., Schafer, R. E.
openaire   +2 more sources

Smooth Tests of Goodness of Fit

Technometrics, 1991
AbstractSmooth tests of goodness of fit assess the fit of data to a given probability density function within a class of alternatives that differs ‘smoothly’ from the null model. These alternatives are characterized by their order: the greater the order the richer the class of alternatives. The order may be a specified constant, but data‐driven methods
Rayner, J. C. W., Thas, O., Best, D. J.
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On the Robustness of Kernel Goodness-of-Fit Tests

Journal of machine learning research
Goodness-of-fit testing is often criticized for its lack of practical relevance: since ``all models are wrong'', the null hypothesis that the data conform to our model is ultimately always rejected as the sample size grows.
Xing Liu, F. Briol
semanticscholar   +1 more source