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primary 59 Copula 15 Asymptotic normality 14 Variable selection 14 Bootstrap 12 Order statistics 11 Consistency 10 Covariance matrix 10 Dimension reduction 10 Longitudinal data 10 Nonparametric regression 10 asymptotic normality 10 EM algorithm 9 Empirical likelihood 9 Oracle property 9 Robust estimation 9 Hypothesis testing 8 Kernel smoothing 8 Nonparametric estimation 8 Elliptical distribution 7 Elliptically contoured distribution 7 Empirical Bayes 7 High-dimensional data 7 Multivariate normal distribution 7 Sufficient dimension reduction 7 Central limit theorem 6 Linear mixed model 6 Model selection 6 Principal component analysis 6 SCAD 6 Sliced inverse regression 6 U-statistic 6 Dirichlet distribution 5 Discriminant analysis 5 Estimating equations 5 Heteroscedasticity 5 Majorization 5 Markov chain Monte Carlo 5 Maximum likelihood estimator 5 Missing at random 5
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Undetermined 3,562
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Article 3,562
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Hall, Peter 26 Fujikoshi, Yasunori 24 Balakrishnan, N. 23 Kubokawa, Tatsuya 20 Khatri, C. G. 19 Krishnaiah, P. R. 19 Sen, Pranab Kumar 19 Takemura, Akimichi 18 Bai, Z. D. 17 Horváth, Lajos 16 Srivastava, M. S. 16 Strawderman, William E. 16 Wang, Qihua 16 Puri, Madan L. 15 Rao, C. Radhakrishna 15 Srivastava, Muni S. 15 Zhu, Lixing 15 Ghosh, Malay 13 Gupta, Arjun K. 13 Joe, Harry 13 Zhu, Li-Xing 12 Hu, Taizhong 11 Nadarajah, Saralees 11 Richards, Donald St. P. 11 Shaked, Moshe 11 Tsai, Ming-Tien 11 You, Jinhong 11 Arellano-Valle, Reinaldo B. 10 Boente, Graciela 10 Fourdrinier, Dominique 10 Genton, Marc G. 10 Lian, Heng 10 Scarsini, Marco 10 Díaz-García, José A. 9 Kariya, Takeaki 9 Mathew, Thomas 9 Peng, Liang 9 Silverstein, Jack W. 9 von Rosen, Dietrich 9 Chikuse, Yasuko 8
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Journal of Multivariate Analysis 3,562
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RePEc 3,562
Showing 3,431 - 3,440 of 3,562
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Minimax estimators of the multinormal mean: Autoregressive priors
Haff, L. R. - In: Journal of Multivariate Analysis 6 (1976) 2, pp. 265-280
Empirical Bayes estimators are given for the mean of a k-dimensional normal distribution, k = 3. We assume that y ~ Nk([theta], V1), V1 = diag(vi), vi known (i = 1, 2,..., k); also, [theta] ~ Nk(0, V2) - V2 defined by one or more unknown parameters. Of particular interest is V2 generated by an...
Persistent link: https://www.econbiz.de/10005153108
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On the ratios of the individual latent roots to the trace of a Wishart matrix
Davis, A. W. - In: Journal of Multivariate Analysis 2 (1972) 4, pp. 440-443
A simple relationship is given between the exact null distribution gm,n(J) of the J-th largest latent root of an m - m Wishart matrix on n degrees of freedom, and the distribution fm,n(J) of the ratio of this root to the trace of the matrix. Explicit expressions for certain fm,n(J) may thus be...
Persistent link: https://www.econbiz.de/10005153136
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Expressions for some hypergeometric functions of matrix argument with applications
Muirhead, Robb J. - In: Journal of Multivariate Analysis 5 (1975) 3, pp. 283-293
Reasonably simple expressions are given for some hypergeometric functions when the size of the argument matrix or matrices is two. Applications of these expressions in connection with the distributions of the latent roots of a 2 - 2 Wishart matrix are also given.
Persistent link: https://www.econbiz.de/10005153142
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Stochastic differential equations
McShane, E. J. - In: Journal of Multivariate Analysis 5 (1975) 2, pp. 121-177
When a system is acted upon by exterior disturbances, its time-development can often be described by a system of ordinary differential equations, provided that the disturbances are smooth functions. But for sound reasons physicists and engineers usually want the theory to apply when the noises...
Persistent link: https://www.econbiz.de/10005153147
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Minimax and admissible minimax estimators of the mean of a multivariate normal distribution for unknown covariance matrix
Alam, Khursheed - In: Journal of Multivariate Analysis 5 (1975) 1, pp. 83-95
Let X be a p-variate (p = 3) vector normally distributed with mean [mu] and covariance [Sigma], and let A be a p - p random matrix distributed independent of X, according to the Wishart distribution W(n, [Sigma]). For estimating [mu], we consider estimators of the form [delta] = [delta](X, A)....
Persistent link: https://www.econbiz.de/10005153150
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Asymptotic nonnull distributions of certain test criteria for a covariance matrix
Nagao, Hisao - In: Journal of Multivariate Analysis 4 (1974) 4, pp. 409-418
Asymptotic expansions of the distributions of two test criteria concerning a covariance matrix are derived under local alternatives in terms of noncentral [chi]2 variates, and under the fixed alternative in terms of standard normal distribution function and its derivatives, respectively. Some...
Persistent link: https://www.econbiz.de/10005153154
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A remark on the tail probability of a distribution
Chen, Robert - In: Journal of Multivariate Analysis 8 (1978) 2, pp. 328-333
Let {Xn}n=1 be a sequence of independent and identically distributed random variables. For each integer n = 1 and positive constants r, t, and [epsilon], let Sn = [Sigma]j=1n Xj and E{N[infinity](r, t, [epsilon])} = [Sigma]n=1[infinity] nr-2P{Sn [epsilon]nr/t}. In this paper, we prove that (1)...
Persistent link: https://www.econbiz.de/10005153155
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Asymptotic expansions for the distributions of some functions of the latent roots of matrices in three situations
Fujikoshi, Y. - In: Journal of Multivariate Analysis 8 (1978) 1, pp. 63-72
In this paper we derive asymptotic expansions for the distributions of some functions of the latent roots of the matrices in three situations in multivariate normal theory, i.e., (i) principal component analysis, (ii) MANOVA model and (iii) canonical correlation analysis. These expansions are...
Persistent link: https://www.econbiz.de/10005153156
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On the Maximum Likelihood estimation of a linear structural relationship when the intercept is known
Chan, Lai K.; Mak, Tak K. - In: Journal of Multivariate Analysis 9 (1979) 2, pp. 304-313
This paper considers the Maximum Likelihood (ML) estimation of the five parameters of a linear structural relationship y = [alpha] + [beta]x when [alpha] is known. The parameters are [beta], the two variances of observation errors on x and y, the mean and variance of x. When the ML estimates of...
Persistent link: https://www.econbiz.de/10005153174
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On the multivariate two-sample problem using strong approximations of the EDF
Burke, Murray D. - In: Journal of Multivariate Analysis 7 (1977) 4, pp. 491-511
The recently developed strong approximation methods are discussed and applied to the problem of testing whether two independent multivariate samples come from the same population and whether the components of the observations are independent. The usual Cramér-von Mises statistic, as well as one...
Persistent link: https://www.econbiz.de/10005153206
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