Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
Estimating the effective dimension reduction (EDR) space, related to the semipara-metric regression model introduced by Li [9], is based on the estimation of the covariance matrix Λ of the conditional expectation of the vector of predictors given the response. An estimator Λn of Λ based on kernel method was introduced by Zhu and Fang [17] who then ...
