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T : 목차정보

Journal of multivariate analysis 16건

  1. [해외논문]   Comparison of a large number of regression curves   SCI SCIE

    Wang, Guanghui (Corresponding author.) , Wang, Zhaojun , Zou, Changliang
    Journal of multivariate analysis v.162 ,pp. 122 - 133 , 2017 , 0047-259x ,

    초록

    Abstract We revisit the classical statistical inference problem of comparing regression curves. Traditional methods assume that the number of curves is small and fixed, while the sample size on which each curve is based tends to infinity. In contrast, we consider the case where the number of curves tends to infinity and the sample sizes are bounded by a common value. Our test is motivated by the fact that two Borel measurable functions are equivalent if and only if their Fourier transforms are identical (Bierens, 1994). An unbiased statistic is then proposed to avoid noise accumulation in a high-dimensional context. The asymptotic null distribution of the test statistic is derived and its power is studied via simulation. An illustration involving cholesterol data is provided.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

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  2. [해외논문]   A sharp boundary for SURE-based admissibility for the normal means problem under unknown scale   SCI SCIE

    Maruyama, Yuzo (Center for Spatial Information Science, University of Tokyo, Tokyo 113–0033, Japan ) , Strawderman, William E. (Department of Statistics, Rutgers University, Piscataway, NJ 08854–8019, USA)
    Journal of multivariate analysis v.162 ,pp. 134 - 151 , 2017 , 0047-259x ,

    초록

    Abstract We consider quasi-admissibility/inadmissibility of Stein-type shrinkage estimators of the mean of a multivariate normal distribution with covariance matrix an unknown multiple of the identity. Quasi-admissibility/inadmissibility is defined in terms of non-existence/existence of a solution to a differential inequality based on Stein’s unbiased risk estimate (SURE). We find a sharp boundary between quasi-admissible and quasi-inadmissible estimators related to the optimal James–Stein estimator. We also find a class of priors related to the Strawderman class in the known variance case where the boundary between quasi-admissibility and quasi-inadmissibility corresponds to the boundary between admissibility and inadmissibility in the known variance case. Additionally, we also briefly consider generalization to the case of general spherically symmetric distributions with a residual vector.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

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  3. [해외논문]   Inference for eigenvalues and eigenvectors in exponential families of random symmetric matrices   SCI SCIE

    Lee, Han Na (Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695-8203, USA ) , Schwartzman, Armin (Division of Biostatistics, University of California, San Diego, 9500 Gilman Drive, MC0631, La Jolla, CA 92093-0631, USA)
    Journal of multivariate analysis v.162 ,pp. 152 - 171 , 2017 , 0047-259x ,

    초록

    Abstract Diffusion tensor imaging (DTI) data consist of a 3 × 3 positive definite random matrix at every voxel. Motivated by the anatomical interpretation of the data, we define a matrix-variate exponential family of distributions for random positive definite matrices and develop estimation and testing procedures for the eigenstructure of the mean parameter. The exponential family includes the spherical Gaussian and matrix-Gamma distributions as special cases. Maximum likelihood estimation and likelihood ratio testing procedures are carried out both in the one-sample and two-sample problems. In addition to their large-sample behavior, their non-asymptotic performance is evaluated via simulations. The methods are illustrated in a real DTI data example.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  4. [해외논문]   Independent component analysis for tensor-valued data   SCI SCIE

    Virta, Joni (Department of Mathematics and Statistics, University of Turku, 20014 Turku, Finland ) , Li, Bing (Department of Statistics, Pennsylvania State University, 326 Thomas Building, University Park, PA 16802, USA ) , Nordhausen, Klaus (Department of Mathematics and Statistics, University of Turku, 20014 Turku, Finland ) , Oja, Hannu (Department of Mathematics and Statistics, University of Turku, 20014 Turku, Finland)
    Journal of multivariate analysis v.162 ,pp. 172 - 192 , 2017 , 0047-259x ,

    초록

    Abstract In preprocessing tensor-valued data, e.g., images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to one of the many methods used for independent component analysis (ICA). However, the tensor structure of the original data is lost in the vectorization and, as a more suitable alternative, we propose the matrix- and tensor fourth order blind identification (MFOBI and TFOBI). In these tensorial extensions of the classic fourth order blind identification (FOBI) we assume a Kronecker structure for the mixing and perform FOBI simultaneously on each direction of the observed tensors. We discuss the theory and assumptions behind MFOBI and TFOBI and provide two different algorithms and related estimates of the unmixing matrices along with their asymptotic properties. Finally, simulations are used to compare the method’s performance with that of classical FOBI for vectorized data and we end with a real data clustering example.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  5. [해외논문]   On additive decompositions of estimators under a multivariate general linear model and its two submodels   SCI SCIE

    Jiang, Bo (College of Mathematics and Information Science, Shandong Institute of Business and Technology, Yantai, China ) , Tian, Yongge (China Economics and Management Academy, Central University of Finance and Economics, Beijing, China)
    Journal of multivariate analysis v.162 ,pp. 193 - 214 , 2017 , 0047-259x ,

    초록

    Abstract Parameters from linear regression models are often estimated by the ordinary least squares estimator (OLSE) or by the best linear unbiased estimator (BLUE). These estimators can be written in analytical form, so that it is not difficult to describe their performances under various model assumptions. In this paper, we study the problem of additive decompositions of OLSEs and BLUEs of parameter spaces in a full multivariate general linear model (MGLM) and in two specific submodels. We establish necessary and sufficient conditions for the validity of various identities involving the OLSEs and BLUEs of whole and partial mean parameter matrices under the MGLM and two smaller MGLMs.

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    무료다운로드 유료다운로드

    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  6. [해외논문]   Estimation and variable selection for quantile partially linear single-index models   SCI SCIE

    Zhang, Yuankun (Department of Mathematical Sciences, University of Cincinnati, OH, USA ) , Lian, Heng (Department of Mathematics, City University of Hong Kong, Hong Kong ) , Yu, Yan (Carl H. Lindner College of Business, University of Cincinnati, OH, USA)
    Journal of multivariate analysis v.162 ,pp. 215 - 234 , 2017 , 0047-259x ,

    초록

    Abstract Partially linear single-index models are flexible dimension reduction semiparametric tools yet still retain ease of interpretability as linear models. This paper is concerned with the estimation and variable selection for partially linear single-index quantile regression models. Polynomial splines are used to estimate the unknown link function. We first establish the asymptotic properties of the quantile regression estimators. For feature selection, we adopt the smoothly clipped absolute deviation penalty (SCAD) approach to select simultaneously single-index variables and partially linear variables. We show that the regularized variable selection estimators are consistent and possess oracle properties. The consistency and oracle properties are also established under the proposed linear approximation of the nonparametric link function that facilitates fast computation. Furthermore, we show that the proposed SCAD tuning parameter selectors via the Schwarz information criterion can consistently identify the true model. Monte Carlo studies and an application to Boston Housing price data are presented to illustrate the proposed approach.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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