Overflow oscillations‐free realization of discrete‐time 2D Roesser models under quantization and overflow constraints

2021 ◽  
Author(s):  
Saddam Hussain Malik ◽  
Muhammad Tufail ◽  
Muhammad Rehan ◽  
Haroon ur Rashid
Keyword(s):  
Discrete Time ◽  
Roesser Models ◽  
Overflow Oscillations

Robust regional stabilization for the two-dimensional mixed continuous-discrete-time Roesser models

2019 ◽  
Vol 37 (3) ◽  
pp. 855-876
Author(s):  
Xiang Ren ◽  
Fei Hao
Keyword(s):  
Discrete Time ◽  
Polynomial Solution ◽  
Linear Matrix ◽  
Sufficient Condition ◽  
Matrix Inequality ◽  
Two Dimensional ◽  
Robust Controller ◽  
Roesser Models ◽  
Regional Stabilization ◽  
Parameter Dependent

Abstract This paper addressed the problem of asymptotic regional stabilization of a class of two-dimensional mixed Roesser models. Based on the analysis of the polynomial solution of the parameter dependent linear matrix inequality (LMI), the sufficient condition for the existence of the regional stabilization controller is obtained in terms of LMI. Moreover, the robust controller is also given to stabilize the systems with uncertainties in the coefficient matrices of the system. Finally, several numerical simulations are provided to illustrate the efficiency and feasibility of the proposed results in this paper.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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