Regression estimation and prediction for discrete time processes

1998 ◽  
pp. 67-87 ◽  
Author(s):  
D. Bosq
Keyword(s):  
Discrete Time ◽  
Regression Estimation ◽  
Estimation And Prediction

scholarly journals ON SEQUENTIAL ESTIMATION AND PREDICTION FOR DISCRETE TIME SERIES

2007 ◽  
Vol 07 (04) ◽  
pp. 417-437 ◽  
Author(s):  
GUSZTÁV MORVAI ◽  
BENJAMIN WEISS
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Recent Work ◽  
Discrete Time ◽  
Sequential Estimation ◽  
Stationary Stochastic Process ◽  
Stopping Times ◽  
Estimation And Prediction ◽  
Discrete Time Series ◽  
Points Of View

The problem of extracting as much information as possible from a sequence of observations of a stationary stochastic process X0,X1,…,Xn has been considered by many authors from different points of view. It has long been known through the work of D. Bailey that no universal estimator for P(Xn+1|X0,X1,…,Xn) can be found which converges to the true estimator almost surely. Despite this result, for restricted classes of processes, or for sequences of estimators along stopping times, universal estimators can be found. We present here a survey of some of the recent work that has been done along these lines.

scholarly journals Regression Estimation and Prediction in Continuous Time

2008 ◽  
Vol 38 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Delphine Blanke ◽  
Denis Bosq
Keyword(s):  
Continuous Time ◽  
Regression Estimation ◽  
Estimation And Prediction

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.

Discrete-Time Delay Dynamics of Boundedly Rational Monopoly

2012 ◽  
Author(s):  
Akio Matsumato ◽  
Ferenc Szidarovsky
Keyword(s):  
Time Delay ◽  
Discrete Time ◽  
Discrete Time Delay
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