A kernel mode estimate under random left truncation and time series model: asymptotic normality

Let Y N , N ≥ 1 be a sequence of random variables of interest and T N , N ≥ 1 be a sequence of truncating variables. For a given n - sample n ≤ N of truncated replicates of Y fulfilling the α - mixing condition, we establish asymptotic normality and construct confidence intervals for a proposed kern...

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Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2015-08, Vol.56 (3), p.887-910
Main Authors: Benrabah, Ouafae, Ould Saïd, Elias, Tatachak, Abdelkader
Format: Article
Language:English
Subjects:
Asymptotic properties
Confidence intervals
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Estimating techniques
Estimators
Finance
Insurance
Kernels
Management
Mathematical models
Mathematics and Statistics
Normality
Operations Research/Decision Theory
Probability Theory and Stochastic Processes
Random variables
Regular Article
Retirement
Sequences
Statistical analysis
Statistics
Statistics for Business
Studies
Texts
Time series
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Summary:Let Y N , N ≥ 1 be a sequence of random variables of interest and T N , N ≥ 1 be a sequence of truncating variables. For a given n - sample n ≤ N of truncated replicates of Y fulfilling the α - mixing condition, we establish asymptotic normality and construct confidence intervals for a proposed kernel mode estimator (say, θ ^ n ) of the true mode θ of Y .
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-014-0613-7