In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model e...In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.展开更多
Letζ =（0,z1,z2,···,zn） with ｜zj｜〈1for1≤j≤n,ω=（1,w1,w2,···,wn）,and P（ζ,ω） denote the set of functions p（z） that are analytic in D={z：｜z｜〈1} and satisfy Rep（z）〉0（｜...Letζ =（0,z1,z2,···,zn） with ｜zj｜〈1for1≤j≤n,ω=（1,w1,w2,···,wn）,and P（ζ,ω） denote the set of functions p（z） that are analytic in D={z：｜z｜〈1} and satisfy Rep（z）〉0（｜z｜〈1）,p（0）=1,p（zj）=wj,j=1,2,···,n.In this article we investigate the extreme points of P（ζ,ω）.展开更多
The main object of the present paper is to investigate a number of useful properties such as inclusion relations, distortion bounds, coefficient estimates, subordination results, the Fekete-Szego problem and some othe...The main object of the present paper is to investigate a number of useful properties such as inclusion relations, distortion bounds, coefficient estimates, subordination results, the Fekete-Szego problem and some other for a new subclass of analytic functions, which are defined here by means of linear operator. Relevant connections of the results presented here with those obtained in earlier works are also pointed out.展开更多
文摘In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.
基金Supported by Educational Commission of Hubei Province of China(D2011006)
文摘Letζ =（0,z1,z2,···,zn） with ｜zj｜〈1for1≤j≤n,ω=（1,w1,w2,···,wn）,and P（ζ,ω） denote the set of functions p（z） that are analytic in D={z：｜z｜〈1} and satisfy Rep（z）〉0（｜z｜〈1）,p（0）=1,p（zj）=wj,j=1,2,···,n.In this article we investigate the extreme points of P（ζ,ω）.
文摘The main object of the present paper is to investigate a number of useful properties such as inclusion relations, distortion bounds, coefficient estimates, subordination results, the Fekete-Szego problem and some other for a new subclass of analytic functions, which are defined here by means of linear operator. Relevant connections of the results presented here with those obtained in earlier works are also pointed out.
