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Moment density estimation for positive random variables.

Authors
Mnatsakanov-RM; Ruymgaart-FH
Source
Stat 2012 Mar-Apr; 46(2):215-230
NIOSHTIC No.
20040915
Abstract
An unknown moment-determinate cumulative distribution function or its density function can be recovered from corresponding moments and estimated from the empirical moments. This method of estimating an unknown density is natural in certain inverse estimation models like multiplicative censoring or biased sampling when the moments of unobserved distribution can be estimated via the transformed moments of the observed distribution. In this paper, we introduce a new nonparametric estimator of a probability density function defined on the positive real line, motivated by the above. Some fundamental properties of proposed estimator are studied. The comparison with traditional kernel density estimator is discussed.
Keywords
Statistical-analysis; Mathematical-models; Models; Sampling; Sampling-methods; Analytical-models; Analytical-processes; Simulation-methods; Quantitative-analysis; Statistical-quality-control; Author Keywords: moment density estimator; mean-squared error; delta-sequence; L-1-consistency
Contact
R.M. Mnatsakanov, West Virginia University, Department of Statistics, POB 6330, Morgantown, WV 26506 USA
CODEN
MOSSD5
Publication Date
20120301
Document Type
Journal Article
Email Address
rmnatsak@stat.wvu.edu
Fiscal Year
2012
NTIS Accession No.
NTIS Price
Identifying No.
B07092012
Issue of Publication
2
ISSN
0233-1888
NIOSH Division
HELD
Source Name
Statistics
State
WV; TX
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