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Order Statistics from the Power Lindley Distribution and Associated Inference with Application
by
Goyal, Anju
, Kumar, Devendra
in
Artificial Intelligence
/ Business and Management
/ Economics
/ Electric power distribution
/ Estimates
/ Finance
/ Insurance
/ Kurtosis
/ Management
/ Mathematical models
/ Maximum likelihood method
/ Parameters
/ Quality control
/ Reliability analysis
/ Samples
/ Statistical analysis
/ Statistical inference
/ Statistical methods
/ Statistics for Business
2019
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Order Statistics from the Power Lindley Distribution and Associated Inference with Application
by
Goyal, Anju
, Kumar, Devendra
in
Artificial Intelligence
/ Business and Management
/ Economics
/ Electric power distribution
/ Estimates
/ Finance
/ Insurance
/ Kurtosis
/ Management
/ Mathematical models
/ Maximum likelihood method
/ Parameters
/ Quality control
/ Reliability analysis
/ Samples
/ Statistical analysis
/ Statistical inference
/ Statistical methods
/ Statistics for Business
2019
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Do you wish to request the book?
Order Statistics from the Power Lindley Distribution and Associated Inference with Application
by
Goyal, Anju
, Kumar, Devendra
in
Artificial Intelligence
/ Business and Management
/ Economics
/ Electric power distribution
/ Estimates
/ Finance
/ Insurance
/ Kurtosis
/ Management
/ Mathematical models
/ Maximum likelihood method
/ Parameters
/ Quality control
/ Reliability analysis
/ Samples
/ Statistical analysis
/ Statistical inference
/ Statistical methods
/ Statistics for Business
2019
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Order Statistics from the Power Lindley Distribution and Associated Inference with Application
Journal Article
Order Statistics from the Power Lindley Distribution and Associated Inference with Application
2019
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Overview
Power Lindley distribution has been proposed recently by Ghitany et al. (Comput Stat Data Anal 64:20–33,
2013
) as a simple and useful reliability model for analysing lifetime data. This model provides more flexibility than the Lindley distribution in terms of the shape of the density and hazard rate functions as well as its skewness and kurtosis. For this distribution, exact explicit expressions for single moments, product moments, marginal moment generating functions and joint moment generating functions of each of these order statistics are derived. By using these relations, we have tabulated the expected values, second moments, variances and covariances of order statistics from samples of sizes up to 10 for various values of the parameters. In addition, we use these moments to obtain the best linear unbiased estimates of the location and scale parameters based on Type-II right-censored samples. In addition, we carry out some numerical illustrations through Monte Carlo simulations to show the usefulness of the findings. Finally, we apply the findings of the paper to some real data set.
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