The Experts below are selected from a list of 148815 Experts worldwide ranked by ideXlab platform
Jiwei Zhao - One of the best experts on this subject based on the ideXlab platform.
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a versatile Estimation Procedure without estimating the nonignorable missingness mechanism
Journal of the American Statistical Association, 2021Co-Authors: Jiwei ZhaoAbstract:We consider the Estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propo...
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a versatile Estimation Procedure without estimating the nonignorable missingness mechanism
arXiv: Methodology, 2019Co-Authors: Jiwei ZhaoAbstract:We consider the Estimation problem in a regression setting where the outcome variable is subject to nonignorable missingness and identifiability is ensured by the shadow variable approach. We propose a versatile Estimation Procedure where modeling of missingness mechanism is completely bypassed. We show that our estimator is easy to implement and we derive the asymptotic theory of the proposed estimator. We also investigate some alternative estimators under different scenarios. Comprehensive simulation studies are conducted to demonstrate the finite sample performance of the method. We apply the estimator to a children's mental health study to illustrate its usefulness.
Wenyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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a dynamic structure for high dimensional covariance matrices and its application in portfolio allocation
Journal of the American Statistical Association, 2017Co-Authors: Shaojun Guo, John Leigh Box, Wenyang ZhangAbstract:Estimation of high-dimensional covariance matrices is an interesting and important research topic. In this article, we propose a dynamic structure and develop an Estimation Procedure for high-dimensional covariance matrices. Asymptotic properties are derived to justify the Estimation Procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high-dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix, the covariance matrix based on factor models, and the shrinkage estimator of covariance matrix. Supplementary materials for this article are available online.
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a dynamic structure for high dimensional covariance matrices and its application in portfolio allocation
arXiv: Methodology, 2015Co-Authors: Shaojun Guo, John Leigh Box, Wenyang ZhangAbstract:Estimation of high dimensional covariance matrices is an interesting and important research topic. In this paper, we propose a dynamic structure and develop an Estimation Procedure for high dimensional covariance matrices. Asymptotic properties are derived to justify the Estimation Procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix and the portfolio allocation proposed in Fan, Fan and Lv (2008).
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statistical Estimation in varying coefficient models
Annals of Statistics, 1999Co-Authors: Jianqing Fan, Wenyang ZhangAbstract:Varying coefficient models are a useful extension of classical linear models. They arise naturally when one wishes to examine how regression coefficients change over different groups characterized by certain covariates such as age. The appeal of these models is that the coefficient functions can easily be estimated via a simple local regression. This yields a simple one-step Estimation Procedure. We show that such a one-step method cannot be optimal when different coefficient functions admit different degrees of smoothness. This drawback can be repaired by using our proposed two-step Estimation Procedure. The asymptotic mean-squared error for the two-step Procedure is obtained and is shown to achieve the optimal rate of convergence. A few simulation studies show that the gain by the two-step Procedure can be quite substantial. The methodology is illustrated by an application to an environmental data set.
Xiangqiang Kong - One of the best experts on this subject based on the ideXlab platform.
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the coupled two step parameter Estimation Procedure for borehole thermal resistance in thermal response test
Renewable Energy, 2020Co-Authors: Changxing Zhang, Shicai Sun, Jianhua Fan, Pengkun Sun, Xiangqiang KongAbstract:Abstract The ground thermal properties and borehole thermal resistance are the essential parameters for the design of borehole heat exchanger (BHE) field, and they are usually estimated using the experimental inlet/outlet fluid temperatures of BHE in thermal response test (TRT). This paper proposes the coupled two-step parameter Estimation Procedure (TSPEP) for estimating ground thermal conductivity and borehole thermal resistance of BHE by evaluating the actual averaged–over-the –depth mean fluid temperature (MFT) using the quasi-three-dimensional model inside the borehole. The simulated annealing algorithm (SAA) is used to iteratively find the minimum values of the two objective functions to obtain the optimal estimated results. In TSPEP, the estimated ground volumetric heat capacity and weighted factor f in the 1st step are transferred to calculate MFT using the experimental data in the 2nd step, which guarantees the direct approach based on the infinite line source model (ILSM)applied to improve the accuracy of the estimated borehole thermal resistance. For 50 m depth BHE, the estimated borehole thermal resistance is increased by 12.1% using TSPEP than the effective borehole thermal resistance evaluated by the arithmetic average fluid temperature (AFT). The estimated ground thermal conductivity in TSPEP is almost same with that from the direct approach based on ILSM, and the maximum relative error between them is only 0.91% even though borehole depth of BHE changes from 50 m to 200 m.
Masahiro Takeuchi - One of the best experts on this subject based on the ideXlab platform.
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a flexible and coherent test Estimation Procedure based on restricted mean survival times for censored time to event data in randomized clinical trials
Statistics in Medicine, 2018Co-Authors: Miki Horiguchi, Angel M Cronin, Masahiro TakeuchiAbstract:: In randomized clinical trials where time-to-event is the primary outcome, almost routinely, the logrank test is prespecified as the primary test and the hazard ratio is used to quantify treatment effect. If the ratio of 2 hazard functions is not constant, the logrank test is not optimal and the interpretation of hazard ratio is not obvious. When such a nonproportional hazards case is expected at the design stage, the conventional practice is to prespecify another member of weighted logrank tests, eg, Peto-Prentice-Wilcoxon test. Alternatively, one may specify a robust test as the primary test, which can capture various patterns of difference between 2 event time distributions. However, most of those tests do not have companion Procedures to quantify the treatment difference, and investigators have fallen back on reporting treatment effect estimates not associated with the primary test. Such incoherence in the "test/Estimation" Procedure may potentially mislead clinicians/patients who have to balance risk-benefit for treatment decision. To address this, we propose a flexible and coherent test/Estimation Procedure based on restricted mean survival time, where the truncation time τ is selected data dependently. The proposed Procedure is composed of a prespecified test and an Estimation of corresponding robust and interpretable quantitative treatment effect. The utility of the new Procedure is demonstrated by numerical studies based on 2 randomized cancer clinical trials; the test is dramatically more powerful than the logrank, Wilcoxon tests, and the restricted mean survival time-based test with a fixed τ, for the patterns of difference seen in these cancer clinical trials.
Yacine Aitsahalia - One of the best experts on this subject based on the ideXlab platform.
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nonparametric pricing of interest rate derivative securities
Econometrica, 1996Co-Authors: Yacine AitsahaliaAbstract:The author proposes a nonparametric Estimation Procedure for continuous-time stochastic models. Because prices of derivative securities depend crucially on the form of the instantaneous volatility of the underlying process, he leaves the volatility function unrestricted and estimates it nonparametrically. Only discrete data are used but the Estimation Procedure still does not rely on replacing the continuous-time model by some discrete approximation. Instead, the drift and volatility functions are forced to match the densities of the process. The author estimates the stochastic differential equation followed by the short-term interest rate and computes nonparametric prices for bonds and bond options. Copyright 1996 by The Econometric Society.
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nonparametric pricing of interest rate derivative securities
National Bureau of Economic Research, 1995Co-Authors: Yacine AitsahaliaAbstract:We propose a nonparametric Estimation Procedure for continuous- time stochastic models. Because prices of derivative securities depend crucially on the form of the instantaneous volatility of the underlying process, we leave the volatility function unrestricted and estimate it nonparametrically. Only discrete data are used but the Estimation Procedure still does not rely on replacing the continuous- time model by some discrete approximation. Instead the drift and volatility functions are forced to match the densities of the process. We estimate the stochastic differential equation followed by the short term interest rate and compute nonparametric prices for bonds and bond options.
