Model checking for general linear error-in-covariables model with validation data

In this paper, model checking problem is considered for general linear model when covariables are measured with error and an independent validation data set is available. Without assuming any error model structure between the true variable and the surrogate variable, the author first apply nonparametric method to model the relationship between the true variable and the surrogate variable with the help of the validation sample. Then the author construct a score-type test statistic through model adjustment. The large sample behaviors of the score-type test statistic are investigated. It is shown that the test is consistent and can detect the alternative hypothesis close to the null hypothesis at the rate n ^−r with 0 ≤ r ≤ 1/2. Simulation results indicate that the proposed method works well.     

Approximate conditional likelihood for generalized linear models with general missing data mechanism

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate. Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.     

Sieve least squares estimator for partial linear models with current status data

Current status data often arise in survival analysis and reliability studies, when a continuous response is reduced to an indicator of whether the response is greater or less than an observed random threshold value. This article considers a partial linear model with current status data. A sieve least squares estimator is proposed to estimate both the regression parameters and the nonparametric function. This paper shows, under some mild condition, that the estimators are strong consistent. Moreover, the parameter estimators are normally distributed, while the nonparametric component achieves the optimal convergence rate. Simulation studies are carried out to investigate the performance of the proposed estimates. For illustration purposes, the method is applied to a real dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.     

Empirical likelihood confidence region for parameters in linear errors-in-variables models with missing data

The multivariate linear errors-in-variables model when the regressors are missing at random in the sense of Rubin (1976) is considered in this paper. A constrained empirical likelihood confidence region for a parameter β ₀ in this model is proposed, which is constructed by combining the score function corresponding to the weighted squared orthogonal distance based on inverse probability with a constrained region of β ₀. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard chi-square distribution. Simulations show that the coverage rate of the proposed confidence region is closer to the nominal level and the length of confidence interval is narrower than those of the normal approximation of inverse probability weighted adjusted least square estimator in most cases. A real example is studied and the result supports the theory and simulation’s conclusion.     

EMMD-Prony approach for dynamic validation of simulation models

Model validation and updating is critical to model credibility growth. In order to assess model credibility quantitatively and locate model error precisely, a new dynamic validation method based on extremum field mean mode decomposition(EMMD) and the Prony method is proposed in this paper. Firstly, complex dynamic responses from models and real systems are processed into stationary components by EMMD. These components always have definite physical meanings which can be the evidence about rough model error location. Secondly, the Prony method is applied to identify the features of each EMMD component. Amplitude similarity, frequency similarity, damping similarity and phase similarity are defined to describe the similarity of dynamic responses.Then quantitative validation metrics are obtained based on the improved entropy weight and energy proportion. Precise model error location is realized based on the physical meanings of these features. The application of this method in aircraft controller design provides evidence about its feasibility and usability.     

TOPSIS价值函数模型

综合研究了TOPSIS函数的结构和性质，在构建单向规范化向量空间的基础上，定义并归纳了6种TOPSIS函数，证明了这6种函数都是价值函数，最后经过分析比较，推荐其中两种函数作为正统的TOPSIS价值函数模型。     

基于数值转换的隐私保护线性回归方法

企业合作进行数据分析时,很多时候不愿意透漏各自的私有数据.针对这种情况提出了在数据异构分布下通过线性转化和添加随机噪声两种数值转换方法来保护企业的私有数据,阐述了怎样在转换后的数据中得到原始的线性回归方程以及线性回归方程拟合优度的计算,怎样在转换后的数据中对回归方程的线性关系和回归系数进行显著性检验,最后结合算例对这两种数值转换方法进行了分析和对比,实验证明基于线性转化和添加随机噪声的两种数值转换方法可用于解决企业合作中私有数据的线性回归问题.     

Two-step estimators in partial linear models with missing response variables and error-prone covariates

A partial linear model with missing response variables and error-prone covariates is considered. The imputation approach is developed to estimate the regression coefficients and the nonparametric function. The proposed parametric estimators are shown to be asymptotically normal, and the estimators for the nonparametric part are proved to converge at an optimal rate. To construct confidence regions for the regression coefficients and the nonparametric function, respectively, the authors also propose the empirical-likelihood-based statistics and investigate the limit distributions of the empirical likelihood ratios. The simulation study is conducted to compare the finite sample behavior for the proposed estimators. An application to an AIDS dataset is illustrated.     

条件线性高斯模型的Gauss Hermite filter-Kalman filter算法

针对条件线性高斯状态空间模型,提出了高斯厄密特滤波-卡尔曼滤波(Gauss Hermite filter-Kalmanfilter,GHF-KF)滤波算法。算法将模型中的条件线性状态方程代入观测方程,并融合线性状态的过程噪声和观测噪声,由GHF获得非线性状态的估计;再将非线性状态的估计均值代入线性状态方程与观测方程,由KF获得线性状态的估计;获得的非线性状态估计方差还用于修正由KF估计的线性状态,以提高精度。将GHF-KF算法应用于目标跟踪的仿真结果表明,与现有Rao-Blackwellized粒子滤波器RBPF相比,新方法在保证估计精度的同时,明显提高了实时性,计算时间仅约为RBPF的5%。     

Empirical likelihood for partially linear models under negatively associated errors

This paper proposes to use the blockwise empirical likelihood(EL) method to construct the confidence regions for the regression vector β in a partially linear model under negatively associated errors. It is shown that the blockwise EL ratio statistic for β is asymptotically χ~2 distributed. The result is used to obtain an EL-based confidence region for β. Results of a simulation study on the finite sample performance of the proposed confidence regions are reported.     

Generalized F-test for high dimensional regression coefficients of partially linear models

This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown nonlinear component by some nonparametric methods and then generalize the F-statistic to test the regression coefficients under some regular conditions. During this procedure, the estimation of the nonlinear component brings much challenge to explore the properties of generalized F-test. The authors obtain some asymptotic properties of the generalized F-test in more general cases,including the asymptotic normality and the power of this test with p/n ∈(0, 1) without normality assumption. The asymptotic result is general and by adding some constraint conditions we can obtain the similar conclusions in high dimensional linear models. Through simulation studies, the authors demonstrate good finite-sample performance of the proposed test in comparison with the theoretical results. The practical utility of our method is illustrated by a real data example.     

Improved anovae of the covariance matrix in general linear mixed models

In this paper, the problem of estimating the covariance matrix in general linear mixed models is considered. A new class of estimators is proposed. It is shown that this new estimator dominates the analysis of variance estimate under two squared loss functions. Finally, some simulation results to compare the performance of the proposed estimator with that of the analysis of variance estimate are reported. The simulation results indicate that this new estimator provides a substantial improvement in risk under most situations.     

Semiparametric Bayesian inference for accelerated failure time models with errors-in-covariates and doubly censored data

This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression errors via the Dirichlet processes. Moreover, the authors extend the Bayesian Lasso approach to our semiparametric model for variable selection. The authors develop the Markov chain Monte Carlo strategies for posterior calculation. Simulation studies are conducted to show the performance of the proposed method. The authors also demonstrate the implementation of the method using analysis of PBC data and ACTG 175 data.     

Inference on coefficient function for varying-coefficient partially linear model

One important model in handling the multivariate data is the varying-coemcient partially linear regression model.In this paper,the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not.It is showed that the normalized proposed test follows asymptoticallyχ~2-distribution and the Wilks phenomenon under the null hypothesis,and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing.Some simulation studies illustrate that the test works well.     

Partially Observed Risk-Sensitive Stochastic Control Problems with Non-Convexity Restriction

The paper considers partially observed optimal control problems for risk-sensitive stochastic systems, where the control domain is non-convex and the diffusion term contains the control v.Utilizing Girsanov’s theorem, spike variational technique as well as duality method, the authors obtain four adjoint equations and establish a maximum principle under partial information. As an application,an example is presented to demonstrate the result.     

Design of Luenberger function observer with disturbance decoupling for matrix second-order linear systems-a parametric approach

1.INTRODUCTIONThere have been many approaches to disturbance de-couplinginlinear multivariable control systemdesign.Linnemann[1]proposed a numerically stable algorithmfor disturbance decoupling by measurement feedback.Van Der Woude[2]considered the combined problemof disturbance decoupling and stabilization by mea-surement feedback.Syrmos[3]proposed a numericallyefficient algorithm based on Sylvester equations forthe problemof disturbance decoupling with arbitrarypole placement usingstat…     

Estimation of Partially Linear Panel Data Models with Cross-Sectional Dependence

Journal of Systems Science and Complexity - This paper studies the estimation of the partially linear panel data models, allowing for cross-sectional dependence through a common factors structure....     

Selecting an adaptive sequence for computing recursive M-estimators in multivariate linear regression models

In this paper, the authors consider an adaptive recursive algorithm by selecting an adaptive sequence for computing M-estimators in multivariate linear regression models. Its asymptotic property is investigated. The recursive algorithm given by Miao and Wu (1996) is modified accordingly. Simulation studies of the algorithm is also provided. In addition, the Newton-Raphson iterative algorithm is considered for the purpose of comparison.     

A cross country review of the validation and/or adjustment of census data

This article reviews existing procedures employed by various countries in the evaluation of, and/or adjustment, either of census data, or of population estimates based upon census data. The work was carried out to ensure all potential demographic techniques are considered by the ONS for the post census evaluation process of the 2011 Census.     

Two New Methods for Multiattribute Decision Makings with Information Partially Known

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