scholarly journals Reconstruction of irregularly sampled discrete-time bandlimited signals with unknown sampling locations

2000 ◽  
Vol 48 (12) ◽  
pp. 3462-3471 ◽  
Author(s):  
M. Vetterli ◽  
P. Marziliano
Keyword(s):  
Discrete Time ◽  
Bandlimited Signals ◽  
Sampling Locations

scholarly journals On recursive stopping of decimation of discrete-time bandlimited signals

2011 ◽  
Vol 52 ◽  
Author(s):  
Rimantas Pupeikis
Keyword(s):  
Fourier Series ◽  
Discrete Time ◽  
Reverse Order ◽  
Nyquist Frequency ◽  
Order Processing ◽  
Series Coefficient ◽  
Bandlimited Signals ◽  
Bandlimited Signal ◽  
Simulation Results ◽  
The Given

For each non-decimated as well as decimated realization discrete-time Fourier series coefficient values, located at Nyquist frequency are calculated, using original speedy recursive expressions based on reverse order processing of the given realizations. The criterion for stopping of multifold decimation of discrete-time bandlimited signals has been developed. The simulation results for the bandlimited signal with a triangularshaped spectrum are presented.  

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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