The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Kung-jong Lui - One of the best experts on this subject based on the ideXlab platform.
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Notes on estimation in Poisson frequency data under an incomplete block crossover design
Statistical Methodology, 2016Co-Authors: Kung-jong LuiAbstract:Abstract For comparison of two experimental treatments with a placebo under an incomplete block crossover design, we develop the weighted-least-squares Estimator (WLSE) and the conditional maximum likelihood Estimator (CMLE) of the relative treatment effects in Poisson frequency data. We further develop the Interval Estimator based on the WLSE, the Interval Estimator based on the CMLE, the Interval Estimator based on the conditional-likelihood-ratio test and the Interval Estimator based on the exact conditional distribution. Using Monte Carlo simulations, we find that all Interval Estimators developed here can perform well in a variety of situations. The exact Interval Estimator derived here can be especially of use when both the number of patients and the mean number of event occurrences are small in a trial. We use the data taken as part of a double-blind randomized crossover trial comparing salbutamol and salmeterol with a placebo with respect to the number of exacerbations in asthma patients to illustrate the use of these Estimators.
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Notes on Interval Estimation of Odds Ratio in Matched Pairs under Stratified Sampling
Communications in Statistics - Simulation and Computation, 2014Co-Authors: Kung-jong LuiAbstract:We develop four asymptotic Interval Estimators and one exact Interval Estimator for the odds ratio (OR) under stratified random sampling with matched pairs. We apply Monte Carlo simulation to evaluate the performance of these five Interval Estimators. We note that the conditional score test-based Interval Estimator with a monotonic transformation and the Interval Estimator based on the Mantel–Haenszel (MH) type point Estimator with the logarithmic transformation are generally preferable to the others considered here. We also note that the conditional exact confidence Interval can be of use when the total number of matched pairs with discordant responses is small.
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Five Interval Estimators of the risk difference under stratified randomized clinical trials with noncompliance and repeated measurements.
Journal of biopharmaceutical statistics, 2013Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:We often employ stratified analysis to control the confounding effect due to centers in a multicenter trial or the confounding effect due to trials in a meta-analysis. On the basis of a general risk additive model, we focus discussion on Interval estimation of the risk difference (RD) in repeated binary measurements under a stratified randomized clinical trial (RCT) in the presence of noncompliance. We develop five asymptotic Interval Estimators for the RD in closed form. These include the Interval Estimator using the weighted least-squares (WLS) Estimator, the WLS Interval Estimator with tanh −1(x) transformation, the Mantel–Haenszel (MH) type Interval Estimator, the MH Interval Estimator with tanh −1(x) transformation, and the Interval Estimator using the idea of Fieller's theorem and a randomization-based variance. We employ Monte Carlo simulation to study and compare the finite-sample performance of these Interval Estimators in a variety of situations. We include an example studying the use of macroph...
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Notes on Interval Estimation of the Generalized Odds Ratio Under Stratified Random Sampling
Journal of biopharmaceutical statistics, 2013Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:It is not rare to encounter the patient response on the ordinal scale in a randomized clinical trial (RCT). Under the assumption that the generalized odds ratio (GOR) is homogeneous across strata, we consider four asymptotic Interval Estimators for the GOR under stratified random sampling. These include the Interval Estimator using the weighted-least-squares (WLS) approach with the logarithmic transformation (WLSL), the Interval Estimator using the Mantel-Haenszel (MH) type of Estimator with the logarithmic transformation (MHL), the Interval Estimator using Fieller's theorem with the MH weights (FTMH) and the Interval Estimator using Fieller's theorem with the WLS weights (FTWLS). We employ Monte Carlo simulation to evaluate the performance of these Interval Estimators by calculating the coverage probability and the average length. To study the bias of these Interval Estimators, we also calculate and compare the noncoverage probabilities in the two tails of the resulting confidence Intervals. We find that...
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A semi-parametric approach to the frequency of occurrence under a simple crossover trial.
Statistical methods in medical research, 2012Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:To analyze the frequency of occurrence for an event of interest in a crossover design, we propose a semi-parametric approach. We develop two point Estimators and four Interval Estimators in closed forms for the treatment effect under a random effects multiplicative risk model. Using Monte Carlo simulations, we evaluate these Estimators and compare the four Interval Estimators with the classical Interval Estimator suggested elsewhere in a variety of situations. We note that the point Estimator using the ratio of two arithmetic averages of mean frequencies under a multiplicative risk model can be comparable to the point Estimator using the ratio of two geometric averages of mean frequencies. We note that as long as the number of patients per group is large, all the four Interval Estimators developed here can perform well. We also note that the classical Interval Estimator derived under the commonly assumed Poisson distribution for the frequency data can be conservative and lose precision if the Poisson dist...
Kuang-chao Chang - One of the best experts on this subject based on the ideXlab platform.
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Five Interval Estimators of the risk difference under stratified randomized clinical trials with noncompliance and repeated measurements.
Journal of biopharmaceutical statistics, 2013Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:We often employ stratified analysis to control the confounding effect due to centers in a multicenter trial or the confounding effect due to trials in a meta-analysis. On the basis of a general risk additive model, we focus discussion on Interval estimation of the risk difference (RD) in repeated binary measurements under a stratified randomized clinical trial (RCT) in the presence of noncompliance. We develop five asymptotic Interval Estimators for the RD in closed form. These include the Interval Estimator using the weighted least-squares (WLS) Estimator, the WLS Interval Estimator with tanh −1(x) transformation, the Mantel–Haenszel (MH) type Interval Estimator, the MH Interval Estimator with tanh −1(x) transformation, and the Interval Estimator using the idea of Fieller's theorem and a randomization-based variance. We employ Monte Carlo simulation to study and compare the finite-sample performance of these Interval Estimators in a variety of situations. We include an example studying the use of macroph...
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Notes on Interval Estimation of the Generalized Odds Ratio Under Stratified Random Sampling
Journal of biopharmaceutical statistics, 2013Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:It is not rare to encounter the patient response on the ordinal scale in a randomized clinical trial (RCT). Under the assumption that the generalized odds ratio (GOR) is homogeneous across strata, we consider four asymptotic Interval Estimators for the GOR under stratified random sampling. These include the Interval Estimator using the weighted-least-squares (WLS) approach with the logarithmic transformation (WLSL), the Interval Estimator using the Mantel-Haenszel (MH) type of Estimator with the logarithmic transformation (MHL), the Interval Estimator using Fieller's theorem with the MH weights (FTMH) and the Interval Estimator using Fieller's theorem with the WLS weights (FTWLS). We employ Monte Carlo simulation to evaluate the performance of these Interval Estimators by calculating the coverage probability and the average length. To study the bias of these Interval Estimators, we also calculate and compare the noncoverage probabilities in the two tails of the resulting confidence Intervals. We find that...
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A semi-parametric approach to the frequency of occurrence under a simple crossover trial.
Statistical methods in medical research, 2012Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:To analyze the frequency of occurrence for an event of interest in a crossover design, we propose a semi-parametric approach. We develop two point Estimators and four Interval Estimators in closed forms for the treatment effect under a random effects multiplicative risk model. Using Monte Carlo simulations, we evaluate these Estimators and compare the four Interval Estimators with the classical Interval Estimator suggested elsewhere in a variety of situations. We note that the point Estimator using the ratio of two arithmetic averages of mean frequencies under a multiplicative risk model can be comparable to the point Estimator using the ratio of two geometric averages of mean frequencies. We note that as long as the number of patients per group is large, all the four Interval Estimators developed here can perform well. We also note that the classical Interval Estimator derived under the commonly assumed Poisson distribution for the frequency data can be conservative and lose precision if the Poisson dist...
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Interval estimation of odds ratio in a stratified randomized clinical trial with noncompliance
Computational Statistics & Data Analysis, 2009Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:It is not uncommon to encounter a randomized clinical trial (RCT), in which we need to account for both the noncompliance of patients to their assigned treatment and confounders to avoid making a misleading inference. In this paper, we focus our attention on estimation of the relative treatment efficacy measured by the odds ratio (OR) in large strata for a stratified RCT with noncompliance. We have developed five asymptotic Interval Estimators for the OR. We employ Monte Carlo simulation to evaluate the finite-sample performance of these Interval Estimators in a variety of situations. We note that the Interval Estimator using the weighted least squares (WLS) method may perform well when the number of strata is small, but tend to be liberal when the number of strata is large. We find that the Interval Estimator using weights which are not functions of unknown parameters required to be estimated from data can improve the accuracy of the Interval Estimator based on the WLS method, but lose precision. We note that the Estimator using the logarithmic transformation of the WLS point Estimator and the Interval Estimator using the logarithmic transformation of the Mantel-Haenszel (MH) type of point Estimator can perform well with respect to both the coverage probability and the average length in all the situations considered here. We further note that the Interval Estimator derived from a quadratic equation using a randomization-based method can be of use as the number of strata is large. Finally, we use the data taken from a multiple risk factor intervention trial to illustrate the use of Interval Estimators appropriate for being employed when the number of strata is small or moderate.
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Estimation of risk ratio in a noncompliance randomized clinical trial with trichotomous dose levels
Statistical Methodology, 2009Co-Authors: Kung-jong Lui, Kuang-chao ChangAbstract:Abstract In randomized clinical trials (RCTs), we may come across the situation in which some patients do not fully comply with their assigned treatment. For an experimental treatment with trichotomous levels, we derive the maximum likelihood Estimator (MLE) of the risk ratio (RR) per level of dose increase in a RCT with noncompliance. We further develop three asymptotic Interval Estimators for the RR. To evaluate and compare the finite sample performance of these Interval Estimators, we employ Monte Carlo simulation. When the number of patients per treatment is large, we find that all Interval Estimators derived in this paper can perform well. When the number of patients is not large, we find that the Interval Estimator using Wald’s statistic can be liberal, while the Interval Estimator using the logarithmic transformation of the MLE can lose precision. We note that use of a bootstrap variance estimate in this case may alleviate these concerns. We further note that an Interval Estimator combining Interval Estimators using Wald’s statistic and the logarithmic transformation can generally perform well with respect to the coverage probability, and be generally more efficient than Interval Estimators using bootstrap variance estimates when RR>1. Finally, we use the data taken from a study of vitamin A supplementation to reduce mortality in preschool children to illustrate the use of these Estimators.
Tarek Raissi - One of the best experts on this subject based on the ideXlab platform.
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On Fixed-Time Interval Estimation of Discrete-Time Nonlinear Time-Varying Systems With Disturbances
2020Co-Authors: Thach Ngoc Dinh, Frédéric Mazenc, Zhenhua Wang, Tarek RaissiAbstract:The aim of this paper is to cope with estimation issues of discrete-time nonlinear time-varying systems with input and output. Inspired by [12], a new design technique of fixed-time observers is proposed. It relies on the use of past values of the output and the theory of the monotone systems to construct dead bit observer or fixed-time Interval Estimator depending on the absence or the presence of uncertainties. Finally, simulations are conducted to verify the effectiveness of the proposed schemes.
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ACC - On Fixed-Time Interval Estimation of Discrete-Time Nonlinear Time-Varying Systems With Disturbances
2020 American Control Conference (ACC), 2020Co-Authors: Thach Ngoc Dinh, Frédéric Mazenc, Zhenhua Wang, Tarek RaissiAbstract:The aim of this paper is to cope with estimation issues of discrete-time nonlinear time-varying systems with input and output. Inspired by [12], a new design technique of fixed-time observers is proposed. It relies on the use of past values of the output and the theory of the monotone systems to construct dead bit observer or fixed-time Interval Estimator depending on the absence or the presence of uncertainties. Finally, simulations are conducted to verify the effectiveness of the proposed schemes.
Thach Ngoc Dinh - One of the best experts on this subject based on the ideXlab platform.
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On Fixed-Time Interval Estimation of Discrete-Time Nonlinear Time-Varying Systems With Disturbances
2020Co-Authors: Thach Ngoc Dinh, Frédéric Mazenc, Zhenhua Wang, Tarek RaissiAbstract:The aim of this paper is to cope with estimation issues of discrete-time nonlinear time-varying systems with input and output. Inspired by [12], a new design technique of fixed-time observers is proposed. It relies on the use of past values of the output and the theory of the monotone systems to construct dead bit observer or fixed-time Interval Estimator depending on the absence or the presence of uncertainties. Finally, simulations are conducted to verify the effectiveness of the proposed schemes.
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ACC - On Fixed-Time Interval Estimation of Discrete-Time Nonlinear Time-Varying Systems With Disturbances
2020 American Control Conference (ACC), 2020Co-Authors: Thach Ngoc Dinh, Frédéric Mazenc, Zhenhua Wang, Tarek RaissiAbstract:The aim of this paper is to cope with estimation issues of discrete-time nonlinear time-varying systems with input and output. Inspired by [12], a new design technique of fixed-time observers is proposed. It relies on the use of past values of the output and the theory of the monotone systems to construct dead bit observer or fixed-time Interval Estimator depending on the absence or the presence of uncertainties. Finally, simulations are conducted to verify the effectiveness of the proposed schemes.
Vincent Andrieu - One of the best experts on this subject based on the ideXlab platform.
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Interval-valued estimation for discrete-time linear systems: application to switched systems.
2019Co-Authors: Laurent Bako, Vincent AndrieuAbstract:This paper proposes a new framework for constructing Interval Estimator for discrete-time linear systems. A key ingredient of this framework is a representation of Intervals in terms of center and radius. It is shown that such a representation provides a simple and efficient recipe for constructing an Interval Estimator from any classical linear observer. Our main results are (i) the derivation of the tightest Interval Estimator for linear discrete-time systems; (ii) a systematic design method of Interval Estimator (iii) an application to switched linear systems.
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Interval-valued state estimation for linear systems: the tightest Estimator and its relaxations ⋆
Automatica, 2019Co-Authors: Laurent Bako, Vincent AndrieuAbstract:This paper discusses an Interval-valued state estimation framework for linear dynamic systems. In particular, we derive an expression of the tightest possible Interval Estimator in the sense that it is the intersection of all Interval-valued Estimators for the system of interest. However, from a numerical implementation perspective, this Estimator might suffer from a high complexity, at least in the general setting. Therefore, practical implementation might require some over-approximations which would yield a good trade-off between computational complexity and tightness. We discuss a number of such over-approximations. We also consider the general estimation scenario when the system parameters, the initial state, the input signal and the measurement are all uncertain.
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Interval-valued state estimation for linear systems: the tightest Estimator and its relaxations
Automatica, 2019Co-Authors: Laurent Bako, Vincent AndrieuAbstract:This paper discusses an Interval-valued state estimation framework for linear dynamic systems. In particular, we derive an expression of the tightest possible Interval Estimator in the sense that it is the intersection of all Interval-valued Estimators for the system of interest. However, from a numerical implementation perspective, this Estimator might suffer from a high complexity, at least in the general setting. Therefore, practical implementation might require some over-approximations which would yield a good trade-off between computational complexity and tightness. We discuss a number of such over-approximations. We also consider the general estimation scenario when the system parameters, the initial state, the input signal and the measurement are all uncertain.
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On the tightest Interval-valued state Estimator for linear systems
2018Co-Authors: Laurent Bako, Vincent AndrieuAbstract:This paper discusses an Interval-valued state es-timator for linear dynamic systems. In particular, we derive an expression of the tightest possible Interval Estimator in the sense that it is the intersection of all Interval-valued Estimators. This Estimator appears, in a general setting, to be an infinite dimensional dynamic system. Therefore practical implementation requires some over-approximations which would yield a good trade-off between computational complexity and tightness.
