Exponentiated distributions
This book contains completely new results, not found anywhere else. Furthermore, additional results scattered elsewhere in the literature are clearly presented. Several well-known distributions, such as Weibull distributions, Burr type XII exponential distributions, and exponential distributions, an...
Main Author: | AL-Hussaini, Essam K., |
---|---|
Other Authors: | Ahsanullah, Mohammad,, SpringerLink (Online service) |
Format: | eBook |
Language: | English Bengali |
Published: |
Paris :
Atlantis Press,
2015.
|
Physical Description: |
1 online resource (xiii, 139 pages) : color illustrations. |
Series: |
Atlantis studies in probability and statistics ;
volume 5. |
Subjects: |
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100 | 1 | |a AL-Hussaini, Essam K., |e author. | |
245 | 1 | 0 | |a Exponentiated distributions / |c Essam K. AL-Hussaini, Mohammad Ahsanullah. |
264 | 1 | |a Paris : |b Atlantis Press, |c 2015. | |
300 | |a 1 online resource (xiii, 139 pages) : |b color illustrations. | ||
336 | |a text |b txt |2 rdacontent. | ||
337 | |a computer |b c |2 rdamedia. | ||
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490 | 1 | |a Atlantis Studies in Probability and Statistics, |x 1879-6893 ; |v volume 5. | |
504 | |a Includes bibliographical references. | ||
588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed January 28, 2015). | |
505 | 0 | |6 880-01 |a Class of exponential distributions Introduction - Basic properties, estimation and prediction under exponential distributions - Family of exponential Weibull distributions - Family of exponential exponential distributions - Family of type Xii exponential distributions - Finite mixture of exponential distributions. | |
520 | |a This book contains completely new results, not found anywhere else. Furthermore, additional results scattered elsewhere in the literature are clearly presented. Several well-known distributions, such as Weibull distributions, Burr type XII exponential distributions, and exponential distributions, and their properties are demonstrated. Both real and simulated data are analyzed. A series of inferences based on a finite mixture of distributions are also presented. | ||
650 | 0 | |a Distribution (Probability theory) | |
650 | 1 | 0 | |a Statistics. |
650 | 2 | 0 | |a Statistics. |
650 | 2 | 4 | |a Statistics, general. |
650 | 6 | |a Distribution (Théorie des probabilités) | |
650 | 7 | |a distribution (statistics-related concept) |2 aat. | |
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653 | 0 | 0 | |a statistiek. |
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653 | 0 | 0 | |a statistische analyse. |
653 | 0 | 0 | |a statistical analysis. |
653 | 1 | 0 | |a Statistics (General) |
653 | 1 | 0 | |a Statistiek (algemeen) |
700 | 1 | |a Ahsanullah, Mohammad, |e author. | |
710 | 2 | |a SpringerLink (Online service) | |
776 | 0 | 8 | |i Printed edition: |z 9789462390782. |
830 | 0 | |a Atlantis studies in probability and statistics ; |v volume 5. | |
880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Class of Exponentiated Distributions Introduction -- |g 1.1. |t Historical Note and Preview -- |g 1.2. |t Generalized Order Statistics -- |g 1.3. |t Why Use Asymmetric Loss Functions-- |g 1.4. |t Markov Chain Monte Carlo (MCMC) Method -- |g 1.5. |t Bayes Prediction -- |g 1.6. |t Mixtures of Exponentiated Distribution Functions -- |t References -- |g 2. |t Basic Properties, Estimation and Prediction Under Exponentiated Distributions -- |g 2.1. |t Introduction -- |g 2.2. |t Properties of the Exponentiated Class of Distributions -- |g 2.2.1. |t Moments -- |g 2.2.2. |t Quantiles -- |g 2.2.3. |t Mode -- |g 2.2.4. |t Hazard Rate Function -- |g 2.2.5. |t Proportional Reversed Hazard Rate Function -- |g 2.2.6. |t Density Function of the rth m-Generalized Order Statistic -- |g 2.3. |t Estimation of Ü, R(x0), n(x0) (All Parameters of G are Known) -- |g 2.3.1. |t Maximum Likelihood Estimation of Ü, R(x0), n(x0) -- |g 2.3.2. |t Bayes Estimation of Ü, R(x0), n(x0) -- |g 2.4. |t Estimation of (Ü, Ý1 ..., Ýk), RH(x0) and nH(x0) (All Parameters of H are Unknown) -- |g 2.4.1. |t Maximum Likelihood Estimation of (Ü, Ý1 ..., Ýk), RH(x0), nH(x0) -- |g 2.4.2. |t Bayes Estimation of (Ü, Ý1 ..., Ýk), RH(x0), nH(x0) -- |g 2.5. |t Bayes One-Sample Prediction of Future Observables (All Parameters of H are Unknown) -- |g 2.5.1. |t One-Sample Scheme -- |g 2.6. |t Numerical Computations Applied to Three Examples -- |t References -- |g 3. |t Family of Exponentiated Weibull Distributions -- |g 3.1. |t Introduction -- |g 3.2. |t Properties of the Exponentiated Weibull Family -- |g 3.2.1. |t Moments -- |g 3.2.2. |t Mean Residual Life (MRL) Function -- |g 3.2.3. |t Quantiles -- |g 3.2.4. |t Modes -- |g 3.2.5. |t Hazard Rate Function -- |g 3.2.6. |t Proportional Reversed Hazard Rate Function -- |g 3.2.7. |t Density Function of the rth m-Generalized Order Statistic -- |g 3.3. |t Estimation of Ü, Ý1, Ý2, RH(x0) and nH(x0), (Allparameters are Unknown) -- |g 3.3.1. |t Maximum Likelihood Estimation -- |g 3.3.2. |t Fisher Information Matrix -- |g 3.3.3. |t Bayes Estimation of Ü, Ý1, Ý2, RH(x0), n(x0) -- |g 3.4. |t Bayes Prediction of Future Observables -- |g 3.5. |t Related Distributions to the EW Family -- |g 3.6. |t Applications -- |t References -- |g 4. |t Family of Exponentiated Exponential Distribution -- |g 4.1. |t Introduction -- |g 4.2. |t Stress-Strength Reliability -- |g 4.3. |t Entropy -- |g 4.4. |t Moments and Cumulants -- |g 4.5. |t Generalized Order Statistics -- |g 4.6. |t Distributions of Sums S2 -- |g 4.6.1. |t Distribution of the Sum Sn -- |g 4.7. |t Distribution of the Product and the Ratio -- |g 4.8. |t Maximum Likelihood Estimation -- |g 4.9. |t Characterization -- |t References -- |g 5. |t Family of Exponentiated Burr Type XII Distributions -- |g 5.1. |t Introduction -- |g 5.2. |t Properties of the Exponentiated Burr XII Distributions -- |g 5.2.1. |t Moments -- |g 5.2.2. |t Mean Residual Life Function -- |g 5.2.3. |t Quantiles -- |g 5.2.4. |t Mode -- |g 5.2.5. |t HRF -- |g 5.2.6. |t Proportional Reversed Hazard Rate Function -- |g 5.2.7. |t Density Function of the rth m-Generalized Order Statistic -- |g 5.3. |t Estimation: All Parameters of H are Unknown -- |g 5.3.1. |t Maximum Likelihood Estimation of (Ü, Ý1, Ý2), RH(x0), nH(x0) -- |g 5.3.2. |t Bayes Estimation of (Ü, Ý1, Ý2), RH(x0), nH(x0) -- |g 5.4. |t Prediction of Future Observables -- |g 5.4.1. |t Random Sample Size -- |g 5.5. |t On Beta---Burr XII Distribution -- |t References -- |g 6. |t Finite Mixture of Exponentiated Distributions -- |g 6.1. |t Introduction -- |g 6.2. |t Properties of Finite Mixtures -- |g 6.2.1. |t Moments -- |g 6.2.2. |t MRLF -- |g 6.2.3. |t HRF -- |g 6.2.4. |t PRHRF -- |g 6.3. |t Point Estimation Based on Balanced Square Error Loss Function -- |g 6.3.1. |t Maximum Likelihood Estimation -- |g 6.3.2. |t Bayes Estimation -- |g 6.4. |t Numerical Example -- |g 6.4.1. |t Point Estimation of the Parameters, SF and HRF -- |g 6.4.2. |t Interval Estimation of the Parameters. |
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