The Experts below are selected from a list of 201948 Experts worldwide ranked by ideXlab platform

Haiying Wang - One of the best experts on this subject based on the ideXlab platform.

  • the focused information criterion for varying coefficient partially Linear Measurement error models
    Statistical Papers, 2016
    Co-Authors: Haiying Wang, Xinjie Chen, Nancy Flournoy
    Abstract:

    Under general parametric models, Claeskens and Hjort (J Am Stat Assoc 98:900–916, 2003) proposed a focused information criterion for model selection which emphasizes the accuracy of estimation for particular parameters of interest. This paper extends their framework to include a semi-parametric varying-coefficient partially Linear model when covariates in both the parametric and the non-parametric parts are subject to Measurement errors. We allow the covariance matrices of the Measurement errors to be unknown and be estimated by replicated observations. Also, we derive the asymptotic properties of the frequentist model average estimator for the model in consideration, which generalizes the results obtained by Wang et al. (Electron J Stat 6:1017–1039, 2012). In addition to asymptotic properties, finite sample performance of the proposed methods are examined in a simulation study, and a data set obtained from Continuing Survey of Food Intakes by Individuals conducted by the U.S. Department of Agriculture’s (CSFII) is considered.

  • adaptive lasso for varying coefficient partially Linear Measurement error models
    Journal of Statistical Planning and Inference, 2013
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    Abstract This paper extends the adaptive LASSO (ALASSO) for simultaneous parameter estimation and variable selection to a varying-coefficient partially Linear model where some of the covariates are subject to Measurement errors of an additive form. We draw comparisons with the SCAD, and prove that both the ALASSO and the SCAD attain the oracle property under this setup. We further develop an algorithm in the spirit of LARS for finding the solution path of the ALASSO in practical applications. Finite sample properties of the proposed methods are examined in a simulation study, and a real data example based on the U.S. Department of Agriculture's Continuing Survey of Food Intakes by Individuals (CSFII) is considered.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varyingcoefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice. AMS 2000 subject classifications: Primary 62E20; secondary 62F10, 62F12.

Alan T K Wan - One of the best experts on this subject based on the ideXlab platform.

  • adaptive lasso for varying coefficient partially Linear Measurement error models
    Journal of Statistical Planning and Inference, 2013
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    Abstract This paper extends the adaptive LASSO (ALASSO) for simultaneous parameter estimation and variable selection to a varying-coefficient partially Linear model where some of the covariates are subject to Measurement errors of an additive form. We draw comparisons with the SCAD, and prove that both the ALASSO and the SCAD attain the oracle property under this setup. We further develop an algorithm in the spirit of LARS for finding the solution path of the ALASSO in practical applications. Finite sample properties of the proposed methods are examined in a simulation study, and a real data example based on the U.S. Department of Agriculture's Continuing Survey of Food Intakes by Individuals (CSFII) is considered.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varyingcoefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice. AMS 2000 subject classifications: Primary 62E20; secondary 62F10, 62F12.

Geert Leus - One of the best experts on this subject based on the ideXlab platform.

  • sparsity promoting sensor selection for non Linear Measurement models
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Sundeep Prabhakar Chepuri, Geert Leus
    Abstract:

    The problem of choosing the best subset of sensors that guarantees a certain estimation performance is referred to as sensor selection. In this paper, we focus on observations that are related to a general non-Linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramer–Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a sparse vector, which in its original form is a nonconvex $\ell_{0}$ -(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. The proposed solvers result in sparse sensing techniques. We also propose a projected subgradient algorithm that is attractive for large-scale problems. The developed theory is applied to sensor placement for localization.

  • sparsity promoting sensor selection for non Linear Measurement models
    arXiv: Information Theory, 2013
    Co-Authors: Sundeep Prabhakar Chepuri, Geert Leus
    Abstract:

    Sensor selection is an important design problem in large-scale sensor networks. Sensor selection can be interpreted as the problem of selecting the best subset of sensors that guarantees a certain estimation performance. We focus on observations that are related to a general non-Linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cramer-Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a selection vector, which in its original form is a nonconvex l0-(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. We also propose a projected subgradient algorithm that is attractive for large-scale problems and also show how the algorithm can be easily distributed. The proposed framework is illustrated with a number of examples related to sensor placement design for localization.

Guohua Zou - One of the best experts on this subject based on the ideXlab platform.

  • adaptive lasso for varying coefficient partially Linear Measurement error models
    Journal of Statistical Planning and Inference, 2013
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    Abstract This paper extends the adaptive LASSO (ALASSO) for simultaneous parameter estimation and variable selection to a varying-coefficient partially Linear model where some of the covariates are subject to Measurement errors of an additive form. We draw comparisons with the SCAD, and prove that both the ALASSO and the SCAD attain the oracle property under this setup. We further develop an algorithm in the spirit of LARS for finding the solution path of the ALASSO in practical applications. Finite sample properties of the proposed methods are examined in a simulation study, and a real data example based on the U.S. Department of Agriculture's Continuing Survey of Food Intakes by Individuals (CSFII) is considered.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varying-coefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice.

  • model averaging for varying coefficient partially Linear Measurement error models
    Electronic Journal of Statistics, 2012
    Co-Authors: Haiying Wang, Guohua Zou, Alan T K Wan
    Abstract:

    In a 2003 paper, Hjort and Claeskens proposed a framework for studying the limiting distributions and asymptotic risk properties of model average estimators under parametric models. They also suggested a simple method for constructing confidence intervals for the parameters of interest estimated by model averaging. The purpose of this paper is to broaden the scope of the aforementioned study to include a semi-parametric varyingcoefficient partially Linear Measurement error model. Within this context, we develop a model averaging scheme for the unknowns, derive the model average estimator’s asymptotic distribution, and develop a confidence interval procedure of the unknowns with an actual coverage probability that tends toward the nominal level in large samples. We further show that confidence intervals that are constructed based on the model average estimators are asymptotically the same as those obtained under the full model. A simulation study examines the finite sample performance of the model average estimators, and a real data analysis illustrates the application of the method in practice. AMS 2000 subject classifications: Primary 62E20; secondary 62F10, 62F12.

Yan Ji - One of the best experts on this subject based on the ideXlab platform.

  • model recovery for hammerstein systems using the auxiliary model based orthogonal matching pursuit method
    Applied Mathematical Modelling, 2018
    Co-Authors: Dongqing Wang, Liwei Li, Yan Ji
    Abstract:

    Abstract This article investigates parameter and order identification of a block-oriented Hammerstein system by using the orthogonal matching pursuit method in the compressive sensing theory which deals with how to recover a sparse signal in a known basis with a Linear Measurement model and a small set of Linear Measurements. The idea is to parameterize the Hammerstein system into the Linear Measurement model containing a Measurement matrix with some unknown variables and a sparse parameter vector by using the key variable separation principle, then an auxiliary model based orthogonal matching pursuit algorithm is presented to recover the sparse vector. The standard orthogonal matching pursuit algorithm with a known Measurement matrix is a popular recovery strategy by picking the supporting basis and the corresponding non-zero element of a sparse signal in a greedy fashion. In contrast to this, the auxiliary model based orthogonal matching pursuit algorithm has unknown variables in the Measurement matrix. For a K -sparse signal, the standard orthogonal matching pursuit algorithm takes a fixed number of K stages to pick K columns (atoms) in the Measurement matrix, while the auxiliary model based orthogonal matching pursuit algorithm takes steps larger than K to pick K atoms in the Measurement matrix with the process of picking and deleting atoms, due to the gradually accurate estimates of the unknown variables step by step. The auxiliary model based orthogonal matching pursuit algorithm can simultaneously identify parameters and orders of the Hammerstein system, and has a high efficient identification performance.