On the H-theorem for the Becker–Döring system of equations for the cases of continuum approximation and discrete time

2020 ◽  
Vol 553 ◽  
pp. 124608
Author(s):  
S.Z. Adzhiev ◽  
I.V. Melikhov ◽  
V.V. Vedenyapin
Keyword(s):  
Discrete Time ◽  
Continuum Approximation ◽  
System Of Equations ◽  
H Theorem

scholarly journals A procedure for distributing recruits in manpower systems

2015 ◽  
Vol 25 (3) ◽  
pp. 445-456 ◽  
Author(s):  
Virtue Ekhosuehi ◽  
Augustine Osagiede ◽  
Wilfred Iguodala
Keyword(s):  
Markov Chain ◽  
Discrete Time ◽  
Academic Staff ◽  
System Of Equations ◽  
Balance Equations ◽  
Fractional Flow ◽  
Discrete Time Markov Chain ◽  
Population Dynamic Model ◽  
Flow Balance ◽  
Staff Mix

In this paper, we treat the following problem: Given a stable Gani-type personflow model and assuming no negative recruitment, what recruitment distribution at the n ? step is capable of generating a staff-mix that closely follows the desired structure? We relate the problem to the challenge of universities in Nigeria towards reaching the desired academic staff-mix by rank specified by the National Universities Commission (NUC). We formulate a population-dynamic model consisting of aggregate-fractional flow balance equations within a discrete-time Markov chain framework for the system. We use MATLas a convenient platform to solve the system of equations. The utility of the model is illustrated by means of academic staff flows in a university-faculty setting in Nigeria.

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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