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作者：AHN, SK
摘要：The asymptotic distribution is derived for residual autocovariances in the multivariate autoregressive model with structured parameterization, a form often employed in multivariate time series modelling to achieve parsimony. It is shown that a similar result for the asymptotic distribution of a portmanteau statistic for the usual full-rank autoregressive-moving average model can be extended to the autoregressive model with structured parameterization.
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作者：GOULD, A; LAWLESS, JF
摘要：The effect on estimation of regression coefficients in linear location-scale models is investigated when the error distribution is misspecified. It is shown that the maximum likelihood estimator under the incorrect model is still consistent. Its asymptotic efficiency relative to that of the maximum likelihood estimator based on the correct model is obtained and the estimation of asymptotic variances for the regression coefficient estimators is addressed.
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作者：BUCKLEY, MJ; EAGLESON, GK; SILVERMAN, BW
作者单位：University of Bath
摘要：A wide class of estimators of the residual variance in nonparametric regression is considered, namely those that are quadratic in the data, unbiased for linear regression, and always nonnegative. The minimax mean squared error estimator over a natural class of regression functions is derived. This optimal estimator has an interesting structure and is closely related to a minimax estimator of the regression curve itself.
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作者：FRANKE, J
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作者：HOSOYA, Y
摘要：The paper presents general second-order approximate formulae for the Fisher information of asymptotically normal statistics, and applies the formulae to a stationary time-series model as well as to an econometric simultaneous equation model. Also the second-order conditional information is evaluated for the exponential family and the location family. Conditioned on a second-order ancillary statistic, the second-order information loss of the maximum likelihood estimate is shown to be recovered.
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作者：BERMAN, M
摘要：A theorem of Jacobi (1841), which shows how the ordinary least-squares estimator from a linear regression can be represented as the weighted sum of ''elementary'' estimators of the parameters, is extended to the generalized least-squares estimator. The generalization is applied to a problem in microwave engineering.
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作者：KENT, JT; OQUIGLEY, J
作者单位：Fred Hutchinson Cancer Center
摘要：In the linear regression model with normal errors the squared product-moment correlation provides the standard measure of dependence between the explanatory variable and the response variable. Using the concept of information gain, a measure of dependence can also be defined for more general regression models used in survival analysis, such as the Weibull regression model or Cox''s proportional hazards model. Further, this measure of dependence can be conveniently estimated even when the respo...
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作者：WOLAK, FA
摘要：This paper derives a duality result for a general class of hypothesis testing problems in multivariate analysis utilizing the relationship between convex cones and their polar cones together with the properties of minimum norm problems between points and cones in Euclidian space. Special cases of this result yield generalizations of a well-known duality relation in multivariate equality constraints testing. For example, any multivariate inequality constraints test on the parameters of a multiv...
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作者：BURKE, MD
摘要：Estimators are proposed of the underlying distribution function of independent bivariate random vectors which are subject to random right censorship in each coordinate. The estimators satisfy the monotonicity requirements of a distribution function, are uniformly strongly consistent at a rate equal to that of the empirical distribution function and are fairly simple to compute.
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作者：RENCHER, AC
摘要：Two major types of canonical functions are considered, canonical discriminant functions that separate groups of observation vectors, and canonical variates associated with canonical correlations. The coefficients in both types of canonical functions reflect the joint contributon of the variables to the canonical functions. If the coefficients are converted to correlations, however, they merely reproduce univariate statistic values and become useless in gauging the importance of each variable i...
