Results 251 to 260 of about 28,805 (302)

Neurophysiological markers of cognitive workload under altered gravity conditions using a gamified dual-task paradigm. [PDF]

Sci Rep
Badalì C, Wollseiffen P, Puck L, Klein T, Schneider S.   +4 more
europepmc   +1 more source

Change Point Detection in Panel Linear Regression Models Based on Jump Information Criterion. [PDF]

Entropy (Basel)
Zhao W, Fan L, Xia Z.
europepmc   +1 more source

Review: A. Wald, Statistical decision functions

, 1951
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Factors associated with postpartum depression in mothers of the kangaroo mother care program in Southern Colombia: a cross-sectional study. [PDF]

Sci Rep
Salazar Herrán RL, Bonilla Santos G, Rocha Perdomo MM, Sánchez Benítez DS, Dussán Chaux JD.   +4 more
europepmc   +1 more source

Review: Abraham Wald, On the Principles of Statistical Inference

, 1942
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A Reminder of the Fallibility of the Wald Statistic

American Statistician, 1996
Abstract Computer programs often produce a parameter estimate and estimated variance (). Thus it is easy to compute a Wald statistic (- θ0){()}−1/2 to test the null hypothesis θ = θ0. Hauck and Donner and Vaeth have identified situations in which the Wald statistic has poor power.
Thomas R Fears, Mitchell H Gail
exaly   +2 more sources

The Exact Distribution of the Wald Statistic: The Non-Central Case [PDF]

, 1984
This paper extends earlier results, which were reported in [7], to include non null distributions. As in [7], attention is concentrated on the Wald statistic for testing general linear restrictions on the coefficients in the multivariate linear model.
Sam Ouliaris, Peter C.B. Phillips
openaire   +2 more sources

Heteroskedasticity testing through comparison of Wald-type statistics [PDF]

, 2011
This paper shows that a test for heteroskedasticity within the context of classical linear regression can be based on the difference between Wald statistics in heteroskedasticity-robust and nonrobust forms. The test is asymptotically distributed under the null hypothesis of homoskedasticity as chi-squared with one degree of freedom.
José Murteira, Esmeralda Ramalho, Joaquim Ramalho   +2 more
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On Wald's equation for U-statistical sums

Statistics & Probability Letters, 1999
Let \(X_1,X_2,\ldots ,X_n\) be i.i.d. random variables taking values in some measurable space and \( {\mathcal F}_n\) be the \(\sigma\)-algebra generated by \(X_1,X_2,\ldots ,X_n\). Denote by \(\tau\) a stopping time adapted to \(\{{\mathcal F}_n\}\).
Borovskikh, Yuri V., Weber, Neville C.
openaire   +2 more sources