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Moment density estimation for positive random variables.
Mnatsakanov RM; Ruymgaart FH
Stat 2012 Mar-Apr; 46(2):215-230
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.
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
R.M. Mnatsakanov, West Virginia University, Department of Statistics, POB 6330, Morgantown, WV 26506 USA
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Page last reviewed: September 2, 2020
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