The present paper deals with the problem of assessing the local influence in a growth curve model with Rao's simple covariance structure. Based on the likelihood displacement,the curvature measure is employed to e...The present paper deals with the problem of assessing the local influence in a growth curve model with Rao's simple covariance structure. Based on the likelihood displacement,the curvature measure is employed to evaluate the effects of some minor perturbations on the statistical inference, thus leading to the large curvature direction, which is the most critical diagnostic statistic in the context of the local influence analysis. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.展开更多
This article considers the admissibility of the linear estimators for the regression coefficients in the growth curve model subject to an incomplete ellipsoidal restriction. The necessary and sufficient conditions for...This article considers the admissibility of the linear estimators for the regression coefficients in the growth curve model subject to an incomplete ellipsoidal restriction. The necessary and sufficient conditions for linear estimators to be admissible in classes of the homogeneous and non-homogeneous linear estimators, respectively, are obtained under the quadratic loss function. They are generalizations of some existing results in literature.展开更多
The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distri...The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distribution assumption, the Kullback-Leibler di- vergence is used to measure the influence of some minor perturbation on the posterior distribution of unknown parameter. This leads to the diagnostic statistic for detecting which response is locally influential. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.展开更多
In this paper, we study the issue of admissibility in the growth curve model with respect to restricted parameter sets under matrix loss function. We obtain some neces- sary and sufficient conditions that the linear e...In this paper, we study the issue of admissibility in the growth curve model with respect to restricted parameter sets under matrix loss function. We obtain some neces- sary and sufficient conditions that the linear estimators of KBL are admissible in the class of homogeneous linear estimators and in the class of non-homogeneous linear estimators under the growth curve model with respect to restricted parameter sets, respectively.展开更多
For the growth curve model with respect to inequality restriction： Y = XBZ ＋ε,ε（0, σ2V I）, trNB ≥0, this paper gives some necessary and sufficient conditions for the linear estimator of KBL to be admissible in...For the growth curve model with respect to inequality restriction： Y = XBZ ＋ε,ε（0, σ2V I）, trNB ≥0, this paper gives some necessary and sufficient conditions for the linear estimator of KBL to be admissible in the class of homogeneous linear estimators LH and nonhomogeneous linear estimators LI, respectively, under the quadratic loss function tr（d（Y） - KBL）＇（d（Y） - KBL）.展开更多
By using the vector-method of matrix, we study Growth Curve Model with respect to linear constraint. Under matrix loss function and vector loss function, we obtain necessary and sufficient conditions for admissibility...By using the vector-method of matrix, we study Growth Curve Model with respect to linear constraint. Under matrix loss function and vector loss function, we obtain necessary and sufficient conditions for admissibility of linear estimators of parameters in the inhomogeneous linear class.展开更多
Abstract： In this paper, we discuss the influence analysis of BLUE in growth curve model with covariate matrix disturbance, and establish the relationships of BLUEs among different models, give the measurement and co...Abstract： In this paper, we discuss the influence analysis of BLUE in growth curve model with covariate matrix disturbance, and establish the relationships of BLUEs among different models, give the measurement and computational formula which can assess the disturbing influence.展开更多
The inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models.This paper presents a two-stage process for analyzing longitudinal textual data,offering a so...The inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models.This paper presents a two-stage process for analyzing longitudinal textual data,offering a solution to this inherent challenge.The proposed model comprises(1)initial data preprocessing and sentiment extraction,followed by(2)applying a growth curve model to analyze the extracted sentiments directly.The paper also explores four distinct approaches for extracting sentiment scores in the dialogue,providing versatility to the proposed framework.The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset.This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data,providing a robust methodology for extracting and analyzing sentiments longitudinally.展开更多
In this paper, we study the issue of admissibility of linear estimated functions of parameters in the multivariate linear model with respect to inequality constraints under a matrix loss and a matrix balanced loss. Un...In this paper, we study the issue of admissibility of linear estimated functions of parameters in the multivariate linear model with respect to inequality constraints under a matrix loss and a matrix balanced loss. Under the matrix loss, when the model is not constrained, the results in the class of non-homogeneous linear estimators [Xie, 1989, Chinese Sci. Bull., 1148-1149; Xie, 1993, J. Multivariate Anal., 1071-1074] showed that the admissibility under the matrix loss and the trace loss is equivalent. However, when the model is constrained by the inequality constraints, we find this equivalency is not tenable, our result shows that the admissibility of linear estimator does not depend on the constraints again under this matrix loss, but it is contrary under the trace loss [Wu, 2008, Linear Algebra Appl., 2040-2048], and it is also relative to the constraints under another matrix loss [He, 2009, Linear Algebra Appl., 241-250]. Under the matrix balanced loss, the necessary and sufficient conditions that the linear estimators are admissible in the class of homogeneous and non-homogeneous linear estimators are obtained, respectively. These results will support the theory of admissibility on the linear model with inequality constraints.展开更多
文摘The present paper deals with the problem of assessing the local influence in a growth curve model with Rao's simple covariance structure. Based on the likelihood displacement,the curvature measure is employed to evaluate the effects of some minor perturbations on the statistical inference, thus leading to the large curvature direction, which is the most critical diagnostic statistic in the context of the local influence analysis. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.
基金Supported by Pre-Study Program of NBRP (2003CCA02400)NSFC (10671007)NSFC (60772036),China
文摘This article considers the admissibility of the linear estimators for the regression coefficients in the growth curve model subject to an incomplete ellipsoidal restriction. The necessary and sufficient conditions for linear estimators to be admissible in classes of the homogeneous and non-homogeneous linear estimators, respectively, are obtained under the quadratic loss function. They are generalizations of some existing results in literature.
基金Supported by the fund of the Yunnan Education Committee!(NO.9941072)
文摘The objective of this paper is to present a Bayesian approach based on Kullback- Leibler divergence for assessing local influence in a growth curve model with general co- variance structure. Under certain prior distribution assumption, the Kullback-Leibler di- vergence is used to measure the influence of some minor perturbation on the posterior distribution of unknown parameter. This leads to the diagnostic statistic for detecting which response is locally influential. As an application, the common covariance-weighted perturbation scheme is thoroughly considered.
基金supported by the NNSF of China(60736047,10671080)NCET(06-672)
文摘In this paper, we study the issue of admissibility in the growth curve model with respect to restricted parameter sets under matrix loss function. We obtain some neces- sary and sufficient conditions that the linear estimators of KBL are admissible in the class of homogeneous linear estimators and in the class of non-homogeneous linear estimators under the growth curve model with respect to restricted parameter sets, respectively.
文摘For the growth curve model with respect to inequality restriction： Y = XBZ ＋ε,ε（0, σ2V I）, trNB ≥0, this paper gives some necessary and sufficient conditions for the linear estimator of KBL to be admissible in the class of homogeneous linear estimators LH and nonhomogeneous linear estimators LI, respectively, under the quadratic loss function tr（d（Y） - KBL）＇（d（Y） - KBL）.
文摘By using the vector-method of matrix, we study Growth Curve Model with respect to linear constraint. Under matrix loss function and vector loss function, we obtain necessary and sufficient conditions for admissibility of linear estimators of parameters in the inhomogeneous linear class.
文摘Abstract： In this paper, we discuss the influence analysis of BLUE in growth curve model with covariate matrix disturbance, and establish the relationships of BLUEs among different models, give the measurement and computational formula which can assess the disturbing influence.
基金supported by the National Science Foundation under Grant No.:SES-1951038.
文摘The inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models.This paper presents a two-stage process for analyzing longitudinal textual data,offering a solution to this inherent challenge.The proposed model comprises(1)initial data preprocessing and sentiment extraction,followed by(2)applying a growth curve model to analyze the extracted sentiments directly.The paper also explores four distinct approaches for extracting sentiment scores in the dialogue,providing versatility to the proposed framework.The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset.This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data,providing a robust methodology for extracting and analyzing sentiments longitudinally.
基金Supported in part by the National Natural Science Foundation of China under Grant No.61070236 and11271147
文摘In this paper, we study the issue of admissibility of linear estimated functions of parameters in the multivariate linear model with respect to inequality constraints under a matrix loss and a matrix balanced loss. Under the matrix loss, when the model is not constrained, the results in the class of non-homogeneous linear estimators [Xie, 1989, Chinese Sci. Bull., 1148-1149; Xie, 1993, J. Multivariate Anal., 1071-1074] showed that the admissibility under the matrix loss and the trace loss is equivalent. However, when the model is constrained by the inequality constraints, we find this equivalency is not tenable, our result shows that the admissibility of linear estimator does not depend on the constraints again under this matrix loss, but it is contrary under the trace loss [Wu, 2008, Linear Algebra Appl., 2040-2048], and it is also relative to the constraints under another matrix loss [He, 2009, Linear Algebra Appl., 241-250]. Under the matrix balanced loss, the necessary and sufficient conditions that the linear estimators are admissible in the class of homogeneous and non-homogeneous linear estimators are obtained, respectively. These results will support the theory of admissibility on the linear model with inequality constraints.
