Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics

J. R. M. Hosking
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 52, No. 1 (1990), pp. 105-124
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2345653
Page Count: 20
  • Read Online (Free)
  • Download ($29.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics
Preview not available

Abstract

L-moments are expectations of certain linear combinations of order statistics. They can be defined for any random variable whose mean exists and form the basis of a general theory which covers the summarization and description of theoretical probability distributions, the summarization and description of observed data samples, estimation of parameters and quantiles of probability distributions, and hypothesis tests for probability distributions. The theory involves such established procedures as the use of order statistics and Gini's mean difference statistic, and gives rise to some promising innovations such as the measures of skewness and kurtosis described in Section 2, and new methods of parameter estimation for several distributions. The theory of L-moments parallels the theory of (conventional) moments, as this list of applications might suggest. The main advantage of L-moments over conventional moments is that L-moments, being linear functions of the data, suffer less from the effects of sampling variability: L-moments are more robust than conventional moments to outliers in the data and enable more secure inferences to be made from small samples about an underlying probability distribution. L-moments sometimes yield more efficient parameter estimates than the maximum likelihood estimates.

Page Thumbnails

  • Thumbnail: Page 
[105]
    [105]
  • Thumbnail: Page 
106
    106
  • Thumbnail: Page 
107
    107
  • Thumbnail: Page 
108
    108
  • Thumbnail: Page 
109
    109
  • Thumbnail: Page 
110
    110
  • Thumbnail: Page 
111
    111
  • Thumbnail: Page 
112
    112
  • Thumbnail: Page 
113
    113
  • Thumbnail: Page 
114
    114
  • Thumbnail: Page 
115
    115
  • Thumbnail: Page 
116
    116
  • Thumbnail: Page 
117
    117
  • Thumbnail: Page 
118
    118
  • Thumbnail: Page 
119
    119
  • Thumbnail: Page 
120
    120
  • Thumbnail: Page 
121
    121
  • Thumbnail: Page 
122
    122
  • Thumbnail: Page 
123
    123
  • Thumbnail: Page 
124
    124