The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Albert Satorra - One of the best experts on this subject based on the ideXlab platform.
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On best affine prediction
Statistical Papers, 2003Co-Authors: Heinz Neudecker, Albert SatorraAbstract:The theory of best affine prediction (BAP) is extended to the vector case with possibly singular Variance Matrix of the predictor variable. The theory is then applied to derive Thomson’s classical predictor for factor scores, allowing for a singular Variance Matrix of the factors. The results are formulated in a free distribution setting. Further, Bartlett’s estimator is considered and compared with Thomson’s predictor.
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The algebraic equality of two asymptotic tests for the hypothesis that a normal distribution has a specified correlation Matrix
Statistics & Probability Letters, 1996Co-Authors: Heinz Neudecker, Albert SatorraAbstract:Abstract We proved the algebraic equality between Jennrich's (1970) asymptotic χ2 test for equality of correlation matrices, and a Wald test statistic derived from the Neudecker and Wesselman (1990) expression of the asymptotic Variance Matrix of the sample correlation Matrix.
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The algebraic equality of two asymptotic tests for the hypothesis that a normal distribution has a specified correlation Matrix
1995Co-Authors: Heinz Neudecker, Albert SatorraAbstract:It is proved the algebraic equality between Jennrich's (1970) asymptotic $X^2$ test for equality of correlation matrices, and a Wald test statistic derived from Neudecker and Wesselman's (1990) expression of the asymptotic Variance Matrix of the sample correlation Matrix.
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The Variance Matrix of sample second-order moments in multivariate linear relations
Statistics & Probability Letters, 1992Co-Authors: Albert SatorraAbstract:Abstract We derive an expression for the Variance Matrix of the vector of (uncentered) sample second-order moments under multivariate linear relations and an independence assumption. An application of the result is presented.
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The Variance Matrix of sample second-order moments in multivariate linear relations
1992Co-Authors: Albert SatorraAbstract:We derive an expression for the Variance Matrix of the vector of (uncentered) sample second-order moments under multivariate linear relations and an independence assumption. An application of the result is presented.(This abstract was borrowed from another version of this item.)
Shyamal D. Peddada - One of the best experts on this subject based on the ideXlab platform.
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Testing for Inequality Constraints in Singular Models by Trimming or Winsorizing the Variance Matrix
Journal of the American Statistical Association, 2018Co-Authors: Ori Davidov, Casey M. Jelsema, Shyamal D. PeddadaAbstract:There are many applications in which a statistic follows, at least asymptotically, a normal distribution with a singular or nearly singular Variance Matrix. A classic example occurs in linear regre...
H. Peter Boswijk - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Testing for Cointegration With Nonstationary Volatility
Journal of Business & Economic Statistics, 2021Co-Authors: H. Peter BoswijkAbstract:This paper develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation Variance Matrix of a vector autoregressi...
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A note on the asymptotics of a stochastic vector difference equation
Biometrika, 1994Co-Authors: H. Peter Boswijk, Heinz Neudecker, Liu ShuangzheAbstract:SUMMARY This note analyses a necessary condition for asymptotic normality of the maximum likelihood estimator in a stationary stochastic vector difference equation. It is shown that this condition is satisfied if the error Variance Matrix is positive definite.
Peter Sasieni - One of the best experts on this subject based on the ideXlab platform.
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Miscellanea. A note on scaled Schoenfeld residuals for the proportional hazards model
Biometrika, 2001Co-Authors: Angela Winnett, Peter SasieniAbstract:Grambsch & Therneau (1994) show how Schoenfeld's partial residuals can be used to diagnose the nature of nonproportional hazards in Cox's (1972) model. Each residual is scaled by pre-multiplying by a time-dependent Variance Matrix, to obtain estimates of time-varying coefficients. Grambsch & Therneau also suggest an approximation in which each residual is scaled using the average Variance Matrix. This approximation is widely used. Here we investigate its reliability across a range of possibilities. In many cases this approximation will make very little difference to the estimates. However, in other cases the approximation may change the scaled residuals considerably and result in misleading estimates of time-varying coefficients. Situations in which the approximation is likely to be inappropriate are discussed, and an analysis of the Mayo clinic lung cancer data is presented as an example in which it is not appropriate. Simple alternative guidelines for using Schoenfeld residuals are provided.
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A note on scaled Schoenfeld residuals for the proportional hazards model
2001Co-Authors: Angela Winnett, Peter SasieniAbstract:SUMMARY Grambsch & Therneau (1994) show how Schoenfeld's partial residuals can be used to diagnose the nature of nonproportional hazards in Cox's (1972) model. Each residual is scaled by premultiplying by a time-dependent Variance Matrix, to obtain estimates of time-varying coefficients. Grambsch & Therneau also suggest an approximation in which each residual is scaled using the average Variance Matrix. This approximation is widely used. Here we investigate its reliability across a range of possibilities. In many cases this approximation will make very little difference to the estimates. However, in other cases the approximation may change the scaled residuals considerably and result in misleading estimates of time-varying coefficients. Situations in which the approximation is likely to be inappropriate are discussed, and an analysis of the Mayo clinic lung cancer data is presented as an example in which it is not appropriate. Simple alternative guidelines for using Schoenfeld residuals are provided.
Ori Davidov - One of the best experts on this subject based on the ideXlab platform.
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Testing for Inequality Constraints in Singular Models by Trimming or Winsorizing the Variance Matrix
Journal of the American Statistical Association, 2018Co-Authors: Ori Davidov, Casey M. Jelsema, Shyamal D. PeddadaAbstract:There are many applications in which a statistic follows, at least asymptotically, a normal distribution with a singular or nearly singular Variance Matrix. A classic example occurs in linear regre...
