scholarly journals On discrete-time laser model with fuzzy environment

2021 ◽  
Vol 6 (4) ◽  
pp. 3105-3120
Author(s):  
Qianhong Zhang ◽  
◽  
Ouyang Miao ◽  
Fubiao Lin ◽  
Zhongni Zhang ◽  
...  
Keyword(s):  
Discrete Time ◽  
Fuzzy Environment ◽  
Laser Model

scholarly journals On discrete time Beverton-Holt population model with fuzzy environment

2019 ◽  
Vol 16 (3) ◽  
pp. 1471-1488 ◽  
Author(s):  
Qianhong Zhang ◽  
◽  
Fubiao Lin ◽  
Xiaoying Zhong ◽  
Keyword(s):  
Discrete Time ◽  
Population Model ◽  
Fuzzy Environment

scholarly journals Asymptotic Behavior of Discrete Time Fuzzy Single Species Model

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Qianhong Zhang ◽  
Fubiao Lin ◽  
Xiaoying Zhong
Keyword(s):  
Asymptotic Behavior ◽  
Positive Solution ◽  
Discrete Time ◽  
Single Species ◽  
Qualitative Behavior ◽  
Initial Value ◽  
Fuzzy Environment ◽  
Surrounding Environment ◽  
Number Of Individuals ◽  
Single Species Model

This work is concerned with the qualitative behavior of discrete time single species model with fuzzy environment xn+1=xnexp⁡A-Bxn,  n=0,1,2,…, where xn denotes the number of individuals of generation n, A is the intrinsic growth rate, and B is interpreted as the carrying capacity of the surrounding environment. xn is a sequence of positive fuzzy number. A,B and the initial value x0 are positive fuzzy numbers. Applying difference of Hukuhara (H-difference), the existence, uniqueness of the positive solution, and global asymptotic behavior of all positive solution with the model are obtained. Moreover a numerical example is presented to show the effectiveness of theoretic results obtained.

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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