In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property...In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.展开更多
We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the s...We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.展开更多
We proposed a new model to price employee stock options （ESOs）. The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modif...We proposed a new model to price employee stock options （ESOs）. The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify Black- Scholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the eady exercise feature, the restriction on shorting selling and the employee＇s risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.展开更多
Monotonic regression problems have been widely seen in many fields like economics and biostatistics.Usually the monotonic parameter space is used by the Bayesian methods using Bernstein polynomials.In this paper the a...Monotonic regression problems have been widely seen in many fields like economics and biostatistics.Usually the monotonic parameter space is used by the Bayesian methods using Bernstein polynomials.In this paper the authors extend the usual parameter space to a larger space in which all the proper parameters making the regression function to be monotonic are included.In order to ensure that the problem could be solved in the new parameter space,the authors use a projection posterior method to make inference.The authors show the proposed method has good approximation properties and performs well compared with other competing methods both in simulations and in practical applications.展开更多
文摘In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.
文摘We propose a flexible joint longitudinal-survival framework to examine the association between longitudinally collected biomarkers and a time-to-event endpoint. More specifically, we use our method for analyzing the survival outcome of end-stage renal disease patients with time-varying serum albumin measurements. Our proposed method is robust to common parametric assumptions in that it avoids explicit specification of the distribution of longitudinal responses and allows for a subject-specific baseline hazard in the survival component. Fully joint estimation is performed to account for uncertainty in the estimated longitudinal biomarkers that are included in the survival model.
基金Funded by the No. 12 Project of Joint Research Projects of Shanghai Stock Exchange with Chongqing University.
文摘We proposed a new model to price employee stock options （ESOs）. The model is based on nonparametric statistical methods with market data. It incorporates the kernel estimator and employs a three-step method to modify Black- Scholes formula. The model overcomes the limits of Black-Scholes formula in handling option prices with varied volatility. It disposes the effects of ESOs self-characteristics such as non-tradability, the longer term for expiration, the eady exercise feature, the restriction on shorting selling and the employee＇s risk aversion on risk neutral pricing condition, and can be applied to ESOs valuation with the explanatory variable in no matter the certainty case or random case.
基金supported by Science Challenge Project under Grant No.TZ2018001。
文摘Monotonic regression problems have been widely seen in many fields like economics and biostatistics.Usually the monotonic parameter space is used by the Bayesian methods using Bernstein polynomials.In this paper the authors extend the usual parameter space to a larger space in which all the proper parameters making the regression function to be monotonic are included.In order to ensure that the problem could be solved in the new parameter space,the authors use a projection posterior method to make inference.The authors show the proposed method has good approximation properties and performs well compared with other competing methods both in simulations and in practical applications.
