Arctic
发表于 2025-3-28 17:16:02
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Transfusion
发表于 2025-3-28 21:57:31
Bayesian Method of Detecting Change Point in Regression and Growth Curve Models,A Bayesian method is given to detect a change point of a regression model when some of the parameters are known a . not to change, while others are subject to change. The same method is applied to a growth curve model. An illustrative numerical example is given.
突袭
发表于 2025-3-29 00:26:00
On Shrinkage and Preliminary Test M-Estimation in a Parallelism Problem,In a multi-sample simple regression model for which homogeneity of the regression slopes is plausible, shrinkage as well as preliminary test versions of .-estimators of the intercepts are considered. In the light of the asymptotic distributional risk, the relative dominance pictures of these estimators are studied.
modest
发表于 2025-3-29 04:19:24
A Bound for the Tail Area of the , Distribution for Samples from a Symmetrically Truncated Normal PIt is shown in this paper that the tail probability of the usual Student’s . statistic under the assumption that the sample arises from a symmetrically truncated normal population, is greater than the corresponding tail probability of the usual .-distribution under normality assumptions for the population for sufficiently large values of ..
bronchiole
发表于 2025-3-29 07:13:42
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Dri727
发表于 2025-3-29 14:27:35
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stress-response
发表于 2025-3-29 19:38:51
Statistical Inference for the Overlap Hypothesis,n between .. and the gain in height, .. - .., or that the correlation between .. and .. is the same as that between .. and .. - ... An estimate of the correlation when either of these hypotheses holds is obtained. A statistical test for the hypothesis is also developed, and indications of a multivariate version are presented.
外露
发表于 2025-3-29 22:53:06
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conifer
发表于 2025-3-30 02:29:58
Testing for the Nullity of the Multiple Correlation Coefficient with Incomplete Multivariate Data,umption that (X., X.,..., X.) has a multivariate normal distribution and a sample of size . is available, where for . observation vectors all components are available, while for . = (.) observation vectors, the data on the last . components, (..,..,...,..), are missing.
Mets552
发表于 2025-3-30 05:38:33
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