Comparisons of Methods of Estimation for the NH Distribution

Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addre...
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Autor*in:	Dey, Sanku [verfasserIn] Zhang, Chunfang Asgharzadeh, A. Ghorbannezhad, M.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Bayes estimators Maximum likelihood estimators Moment estimators Percentile estimators Least square estimators

Anmerkung:	© Springer-Verlag GmbH Germany 2017

Übergeordnetes Werk:	Enthalten in: Annals of data science - Berlin : Springer, 2014, 4(2017), 4 vom: 17. Juli, Seite 441-455
Übergeordnetes Werk:	volume:4 ; year:2017 ; number:4 ; day:17 ; month:07 ; pages:441-455

Links:	Volltext

DOI / URN:	10.1007/s40745-017-0114-3

Katalog-ID:	SPR037215043

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520			\|a Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes.
650		4	\|a Bayes estimators \|7 (dpeaa)DE-He213
650		4	\|a Maximum likelihood estimators \|7 (dpeaa)DE-He213
650		4	\|a Moment estimators \|7 (dpeaa)DE-He213
650		4	\|a Percentile estimators \|7 (dpeaa)DE-He213
650		4	\|a Least square estimators \|7 (dpeaa)DE-He213
700	1		\|a Zhang, Chunfang \|4 aut
700	1		\|a Asgharzadeh, A. \|4 aut
700	1		\|a Ghorbannezhad, M. \|4 aut
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912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_184
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4277
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
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publishDate	2017
allfields	10.1007/s40745-017-0114-3 doi (DE-627)SPR037215043 (SPR)s40745-017-0114-3-e DE-627 ger DE-627 rakwb eng Dey, Sanku verfasserin aut Comparisons of Methods of Estimation for the NH Distribution 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213 Zhang, Chunfang aut Asgharzadeh, A. aut Ghorbannezhad, M. aut Enthalten in Annals of data science Berlin : Springer, 2014 4(2017), 4 vom: 17. Juli, Seite 441-455 (DE-627)795566824 (DE-600)2783277-6 2198-5812 nnns volume:4 year:2017 number:4 day:17 month:07 pages:441-455 https://dx.doi.org/10.1007/s40745-017-0114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2017 4 17 07 441-455
spelling	10.1007/s40745-017-0114-3 doi (DE-627)SPR037215043 (SPR)s40745-017-0114-3-e DE-627 ger DE-627 rakwb eng Dey, Sanku verfasserin aut Comparisons of Methods of Estimation for the NH Distribution 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213 Zhang, Chunfang aut Asgharzadeh, A. aut Ghorbannezhad, M. aut Enthalten in Annals of data science Berlin : Springer, 2014 4(2017), 4 vom: 17. Juli, Seite 441-455 (DE-627)795566824 (DE-600)2783277-6 2198-5812 nnns volume:4 year:2017 number:4 day:17 month:07 pages:441-455 https://dx.doi.org/10.1007/s40745-017-0114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2017 4 17 07 441-455
allfields_unstemmed	10.1007/s40745-017-0114-3 doi (DE-627)SPR037215043 (SPR)s40745-017-0114-3-e DE-627 ger DE-627 rakwb eng Dey, Sanku verfasserin aut Comparisons of Methods of Estimation for the NH Distribution 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213 Zhang, Chunfang aut Asgharzadeh, A. aut Ghorbannezhad, M. aut Enthalten in Annals of data science Berlin : Springer, 2014 4(2017), 4 vom: 17. Juli, Seite 441-455 (DE-627)795566824 (DE-600)2783277-6 2198-5812 nnns volume:4 year:2017 number:4 day:17 month:07 pages:441-455 https://dx.doi.org/10.1007/s40745-017-0114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2017 4 17 07 441-455
allfieldsGer	10.1007/s40745-017-0114-3 doi (DE-627)SPR037215043 (SPR)s40745-017-0114-3-e DE-627 ger DE-627 rakwb eng Dey, Sanku verfasserin aut Comparisons of Methods of Estimation for the NH Distribution 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213 Zhang, Chunfang aut Asgharzadeh, A. aut Ghorbannezhad, M. aut Enthalten in Annals of data science Berlin : Springer, 2014 4(2017), 4 vom: 17. Juli, Seite 441-455 (DE-627)795566824 (DE-600)2783277-6 2198-5812 nnns volume:4 year:2017 number:4 day:17 month:07 pages:441-455 https://dx.doi.org/10.1007/s40745-017-0114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2017 4 17 07 441-455
allfieldsSound	10.1007/s40745-017-0114-3 doi (DE-627)SPR037215043 (SPR)s40745-017-0114-3-e DE-627 ger DE-627 rakwb eng Dey, Sanku verfasserin aut Comparisons of Methods of Estimation for the NH Distribution 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany 2017 Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213 Zhang, Chunfang aut Asgharzadeh, A. aut Ghorbannezhad, M. aut Enthalten in Annals of data science Berlin : Springer, 2014 4(2017), 4 vom: 17. Juli, Seite 441-455 (DE-627)795566824 (DE-600)2783277-6 2198-5812 nnns volume:4 year:2017 number:4 day:17 month:07 pages:441-455 https://dx.doi.org/10.1007/s40745-017-0114-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2017 4 17 07 441-455
language	English
source	Enthalten in Annals of data science 4(2017), 4 vom: 17. Juli, Seite 441-455 volume:4 year:2017 number:4 day:17 month:07 pages:441-455
sourceStr	Enthalten in Annals of data science 4(2017), 4 vom: 17. Juli, Seite 441-455 volume:4 year:2017 number:4 day:17 month:07 pages:441-455
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bayes estimators Maximum likelihood estimators Moment estimators Percentile estimators Least square estimators
isfreeaccess_bool	false
container_title	Annals of data science
authorswithroles_txt_mv	Dey, Sanku @@aut@@ Zhang, Chunfang @@aut@@ Asgharzadeh, A. @@aut@@ Ghorbannezhad, M. @@aut@@
publishDateDaySort_date	2017-07-17T00:00:00Z
hierarchy_top_id	795566824
id	SPR037215043
language_de	englisch
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This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. 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author	Dey, Sanku
spellingShingle	Dey, Sanku misc Bayes estimators misc Maximum likelihood estimators misc Moment estimators misc Percentile estimators misc Least square estimators Comparisons of Methods of Estimation for the NH Distribution
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topic_title	Comparisons of Methods of Estimation for the NH Distribution Bayes estimators (dpeaa)DE-He213 Maximum likelihood estimators (dpeaa)DE-He213 Moment estimators (dpeaa)DE-He213 Percentile estimators (dpeaa)DE-He213 Least square estimators (dpeaa)DE-He213
topic	misc Bayes estimators misc Maximum likelihood estimators misc Moment estimators misc Percentile estimators misc Least square estimators
topic_unstemmed	misc Bayes estimators misc Maximum likelihood estimators misc Moment estimators misc Percentile estimators misc Least square estimators
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title	Comparisons of Methods of Estimation for the NH Distribution
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title_full	Comparisons of Methods of Estimation for the NH Distribution
author_sort	Dey, Sanku
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author_browse	Dey, Sanku Zhang, Chunfang Asgharzadeh, A. Ghorbannezhad, M.
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author-letter	Dey, Sanku
doi_str_mv	10.1007/s40745-017-0114-3
title_sort	comparisons of methods of estimation for the nh distribution
title_auth	Comparisons of Methods of Estimation for the NH Distribution
abstract	Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. © Springer-Verlag GmbH Germany 2017
abstractGer	Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. © Springer-Verlag GmbH Germany 2017
abstract_unstemmed	Abstract The extended exponential distribution due to Nadarajah and Haghighi (Stat J Theor Appl Stat 45(6):543–558, 2011) is an alternative and always provides better fits than the gamma, Weibull and the generalized exponential distributions whenever the data contains zero values. This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. Finally, two real data sets have been analyzed for illustrative purposes. © Springer-Verlag GmbH Germany 2017
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title_short	Comparisons of Methods of Estimation for the NH Distribution
url	https://dx.doi.org/10.1007/s40745-017-0114-3
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author2	Zhang, Chunfang Asgharzadeh, A. Ghorbannezhad, M.
author2Str	Zhang, Chunfang Asgharzadeh, A. Ghorbannezhad, M.
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doi_str	10.1007/s40745-017-0114-3
up_date	2024-07-03T21:43:36.021Z
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This article addresses different methods of estimation of the unknown parameters from both frequentist and Bayesian view points of Nadarajah and Haghighi (in short NH ) distribution. We briefly describe different frequentist approaches, namely, maximum likelihood estimators, moment estimators, percentile estimators, least square and weighted least square estimators and compare them using extensive numerical simulations. Next we consider Bayes estimation under different types of loss functions (symmetric and asymmetric loss functions) using gamma priors for both shape and scale parameters. Besides, the asymptotic confidence intervals, two parametric bootstrap confidence intervals using frequentist approaches are provided to compare with Bayes credible intervals. Furthermore, the Bayes estimators and their respective posterior risks are computed and compared using Markov chain Monte Carlo algorithm. 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