Results 261 to 270 of about 257,982 (300)

‘Wald's Lemma' for sums of order statistics of i.i.d. random variables

Advances in Applied Probability, 1991
Let X1, X2, · ··, Xn be positive i.i.d. random variables with known distribution function having a finite mean. For a given s ≥0 we define Nn = N(n, s) to be the largest number k such that the sum of the smallest k Xs does not exceed s, and Mn = M(n, s) to be the largest number k such that the sum of the largest k X's does not exceed s.
Bruss, F Thomas, Robertson, James B
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Pre-checking for non-monotonicity of the Wald statistic

Journal of Statistical Computation and Simulation, 2005
The non-monotonic behaviour of the Wald test in some finite-sample applications leads to low power when the null hypothesis needs rejection most. This article proposes a simple check for discerning if the Wald statistic for testing significance of regression coefficients is non-monotonic in the neighbourhood of the parameter space from which the sample
Kim-Leng Goh, Maxwell King
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Obtaining any Wald statistic you want

Economics Letters, 1986
Abstract While the Wald test can be used to test a non-linear hypothesis in a linear or non-linear regression model it is known that a particular hypothesis can be written in many ways when non-linear forms are permitted. This paper illustrates that it is possible to obtain virtually any value of the Wald statistic at different significance levels ...
Francine Lafontaine, Kenneth J. White
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Modified Wald statistics for generalized linear models

Allgemeines Statistisches Archiv, 2004
Wald statistics in generalized linear models are asymptotically Χ2 distributed. The asymptotic chi–squared law of the corresponding quadratic form shows disadvantages with respect to the approximation of the finite–sample distribution. It is shown by means of a comprehensive simulation study that improvements can be achieved by applying simple finite ...
Andreas Oelerich, Thorsten Poddig
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A Simulation Study on a Wald Statistic and Cochran's Q Statistic for Stratified Samples

Biometrics, 1977
A Wald statistic is offered to test equality of proportions in matched samples when the probability of "success" of an observation in a given sample is constant within the stratum but possibly different between strata. Cochran's Q statistic might be used to test this hypothesis since it has been used to test equality of proportions in matched samples ...
Grant W. Somes, V. P. Bhapkar
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ON THE ASYMPTOTIC NULL-DISTRIBUTION OF THE WALD STATISTIC AT SINGULAR PARAMETER POINTS

Statistics & Risk Modeling, 1999
Summary: We consider the large sample Wald test for a nonlinear null hypothesis on a multidimensional parameter in a statistical model. The Wald statistic is known to be asymptotically chi-square distributed under the null hypothesis, provided that the Jacobian of the restriction function describing the null hypothesis has full rank. However, there may
Gaffke, Norbert, Steyer, Rolf, von Davier, Alina A.   +2 more
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The Wald Statistic in Proportional Hazards Hypothesis Testing

Biometrical Journal, 1989
AbstractIn survivorship modelling using the proportional hazards model of Cox (1972, Journal of the Royal Statistical Society, Series B, 34, 187–220), it is often desired to test a subset of the vector of unknown regression parameters β in the expression for the hazard rate at timet.
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A Reminder of the Fallibility of the Wald Statistic: Likelihood Explanation

The American Statistician, 2000
Abstract The Wald statistic is one of the most commonly used tools in applied statistics, so it is sobering to read Fears, Benichou, and Gail's recent reminder of its fallibility. What makes their example particularly relevant is the fact that the problem is manifest in a simple normal random effects model on a balanced dataset for a seemingly harmless
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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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The finite-sample distributions of heteroskedasticity robust Wald statistics

Journal of Econometrics, 1991
The Imhof procedure is used to calculate the exact finite-sample distributions of alternative heteroskedasticity robust Wald-type tests of scalar linear hypothesis in the normal linear model. The tests are distinguished by the variance estimator that they use. These include the White-Eicker and jackknife estimators.
Andrew Chesher, Gerard Austin
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