The Experts below are selected from a list of 5385 Experts worldwide ranked by ideXlab platform

Nian-sheng Tang - One of the best experts on this subject based on the ideXlab platform.

  • Confidence Intervals Construction for difference of two means with incomplete correlated data
    BMC, 2016
    Co-Authors: Nian-sheng Tang
    Abstract:

    Abstract Background Incomplete data often arise in various clinical trials such as crossover trials, equivalence trials, and pre and post-test comparative studies. Various methods have been developed to Construct Confidence Interval (CI) of risk difference or risk ratio for incomplete paired binary data. But, there is little works done on incomplete continuous correlated data. To this end, this manuscript aims to develop several approaches to Construct CI of the difference of two means for incomplete continuous correlated data. Methods Large sample method, hybrid method, simple Bootstrap-resampling method based on the maximum likelihood estimates (B 1) and Ekbohm’s unbiased estimator (B 2), and percentile Bootstrap-resampling method based on the maximum likelihood estimates (B 3) and Ekbohm’s unbiased estimator (B 4) are presented to Construct CI of the difference of two means for incomplete continuous correlated data. Simulation studies are conducted to evaluate the performance of the proposed CIs in terms of empirical coverage probability, expected Interval width, and mesial and distal non-coverage probabilities. Results Empirical results show that the Bootstrap-resampling-based CIs B 1, B 2, B 4 behave satisfactorily for small to moderate sample sizes in the sense that their coverage probabilities could be well controlled around the pre-specified nominal Confidence level and the ratio of their mesial non-coverage probabilities to the non-coverage probabilities could be well controlled in the Interval [0.4, 0.6]. Conclusions If one would like a CI with the shortest Interval width, the Bootstrap-resampling-based CIs B 1 is the optimal choice

Zhang Cun-hui - One of the best experts on this subject based on the ideXlab platform.

  • Local Inference in Additive Models with Decorrelated Local Linear Estimator
    2019
    Co-Authors: Guo Zijian, Zhang Cun-hui
    Abstract:

    Additive models, as a natural generalization of linear regression, have played an important role in studying nonlinear relationships. Despite of a rich literature and many recent advances on the topic, the statistical inference problem in additive models is still relatively poorly understood. Motivated by the inference for the exposure effect and other applications, we tackle in this paper the statistical inference problem for $f_1'(x_0)$ in additive models, where $f_1$ denotes the univariate function of interest and $f_1'(x_0)$ denotes its first order derivative evaluated at a specific point $x_0$. The main challenge for this local inference problem is the understanding and control of the additional uncertainty due to the need of estimating other components in the additive model as nuisance functions. To address this, we propose a decorrelated local linear estimator, which is particularly useful in reducing the effect of the nuisance function estimation error on the estimation accuracy of $f'_1(x_0)$. We establish the asymptotic limiting distribution for the proposed estimator and then Construct Confidence Interval and hypothesis testing procedures for $f_1'(x_0)$. The variance level of the proposed estimator is of the same order as that of the local least squares in nonparametric regression, or equivalently the additive model with one component, while the bias of the proposed estimator is jointly determined by the statistical accuracies in estimating the nuisance functions and the relationship between the variable of interest and the nuisance variables. The method is developed for general additive models and is demonstrated in the high-dimensional sparse setting

Guo Zijian - One of the best experts on this subject based on the ideXlab platform.

  • Local Inference in Additive Models with Decorrelated Local Linear Estimator
    2019
    Co-Authors: Guo Zijian, Zhang Cun-hui
    Abstract:

    Additive models, as a natural generalization of linear regression, have played an important role in studying nonlinear relationships. Despite of a rich literature and many recent advances on the topic, the statistical inference problem in additive models is still relatively poorly understood. Motivated by the inference for the exposure effect and other applications, we tackle in this paper the statistical inference problem for $f_1'(x_0)$ in additive models, where $f_1$ denotes the univariate function of interest and $f_1'(x_0)$ denotes its first order derivative evaluated at a specific point $x_0$. The main challenge for this local inference problem is the understanding and control of the additional uncertainty due to the need of estimating other components in the additive model as nuisance functions. To address this, we propose a decorrelated local linear estimator, which is particularly useful in reducing the effect of the nuisance function estimation error on the estimation accuracy of $f'_1(x_0)$. We establish the asymptotic limiting distribution for the proposed estimator and then Construct Confidence Interval and hypothesis testing procedures for $f_1'(x_0)$. The variance level of the proposed estimator is of the same order as that of the local least squares in nonparametric regression, or equivalently the additive model with one component, while the bias of the proposed estimator is jointly determined by the statistical accuracies in estimating the nuisance functions and the relationship between the variable of interest and the nuisance variables. The method is developed for general additive models and is demonstrated in the high-dimensional sparse setting

Satwiko Darmesto - One of the best experts on this subject based on the ideXlab platform.

  • SMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACH
    2016
    Co-Authors: Rita Diana, Nyoman I. Budiantara, Satwiko Darmesto
    Abstract:

    Semiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach cannot be applied for solving the inference problem such as Constructing Confidence Intervals for the regression curve. To Construct Confidence Interval of smoothing spline curve in the semiparametric additive regression model, we propose to use Bayesian approach, by assuming improper Gaussian distribution for prior distribution in nonparametric components and multivariate normal distribution for parametric components. In this study, we obtain parameter estimators for parametric component and smoothing spline estimators for the nonparametric component in semiparametric additive regression model. Moreover, we also develop a smoothing parameters selection method simultaneously using Generalized Maximum Likelihood (GML) and Confidence Intervals for the parameters of the parametric component and the smoothing spline functions of the nonparametric component using Bayesian approach. By computing each posterior mean and posterior variance of parametric componen

Rita Diana - One of the best experts on this subject based on the ideXlab platform.

  • SMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACH
    2016
    Co-Authors: Rita Diana, Nyoman I. Budiantara, Satwiko Darmesto
    Abstract:

    Semiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach cannot be applied for solving the inference problem such as Constructing Confidence Intervals for the regression curve. To Construct Confidence Interval of smoothing spline curve in the semiparametric additive regression model, we propose to use Bayesian approach, by assuming improper Gaussian distribution for prior distribution in nonparametric components and multivariate normal distribution for parametric components. In this study, we obtain parameter estimators for parametric component and smoothing spline estimators for the nonparametric component in semiparametric additive regression model. Moreover, we also develop a smoothing parameters selection method simultaneously using Generalized Maximum Likelihood (GML) and Confidence Intervals for the parameters of the parametric component and the smoothing spline functions of the nonparametric component using Bayesian approach. By computing each posterior mean and posterior variance of parametric componen