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Estimation with Modified Power Function Distribution Based on Order Statistics with Application to Evaporation Data
by
Kumar, Maneesh
, Kumar, Devendra
, Joorel, J. P. Singh
in
Artificial Intelligence
/ Business and Management
/ Data science
/ Economics
/ Electric power distribution
/ Estimates
/ Failure rates
/ Finance
/ Insurance
/ Management
/ Parameters
/ Quality control
/ Random variables
/ Samples
/ Statistical analysis
/ Statistics
/ Statistics for Business
2022
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Estimation with Modified Power Function Distribution Based on Order Statistics with Application to Evaporation Data
by
Kumar, Maneesh
, Kumar, Devendra
, Joorel, J. P. Singh
in
Artificial Intelligence
/ Business and Management
/ Data science
/ Economics
/ Electric power distribution
/ Estimates
/ Failure rates
/ Finance
/ Insurance
/ Management
/ Parameters
/ Quality control
/ Random variables
/ Samples
/ Statistical analysis
/ Statistics
/ Statistics for Business
2022
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Do you wish to request the book?
Estimation with Modified Power Function Distribution Based on Order Statistics with Application to Evaporation Data
by
Kumar, Maneesh
, Kumar, Devendra
, Joorel, J. P. Singh
in
Artificial Intelligence
/ Business and Management
/ Data science
/ Economics
/ Electric power distribution
/ Estimates
/ Failure rates
/ Finance
/ Insurance
/ Management
/ Parameters
/ Quality control
/ Random variables
/ Samples
/ Statistical analysis
/ Statistics
/ Statistics for Business
2022
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Estimation with Modified Power Function Distribution Based on Order Statistics with Application to Evaporation Data
Journal Article
Estimation with Modified Power Function Distribution Based on Order Statistics with Application to Evaporation Data
2022
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Overview
The modified power function distribution is an important distribution for analyzing the lifetime data, which is quite flexible and can be used effectively in modeling survival data. It can have increasing, decreasing, upside-down bathtub and bathtub shaped failure rate. In this paper, we derive the exact explicit expressions for the single and double (product) of order statistics from the modified power function distribution. 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. Also, 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 one real data set.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
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