A new goodness-of-fit test for the inverse Gaussian distribution

Hadi Alızadeh Noughabi; Mohammad Shafaei Noughabi

Makale detayları

A new goodness-of-fit test for the inverse Gaussian distribution

Yazarlar:

Hadi Alızadeh Noughabi | Mohammad Shafaei Noughabi

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics | Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.53 Issue 3, pp.823-836

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320026

pISSN:

2651-477X

Anahtar kelimeler:

Özet:

 The Inverse Gaussian IG distribution is widely used in practice and therefore an important issue is to develop a powerful goodness-of-fit test GOF for this distribution. In this article, we propose and examine a new GOF test for the IG distribution based on a new estimate of Kullback-Leibler KL information. The properties of the test statistic are presented. In order to compute the proposed test statistic, parameters of the IG distribution are estimated by maximum likelihood estimators, which are simple explicit estimators. Critical values and the actual sizes of the proposed test are obtained. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. Finally, two illustrative examples are presented and analyzed.

 The Inverse Gaussian IG distribution is widely used in practice and therefore an important issue is to develop a powerful goodness-of-fit test GOF for this distribution. In this article, we propose and examine a new GOF test for the IG distribution based on a new estimate of Kullback-Leibler KL information. The properties of the test statistic are presented. In order to compute the proposed test statistic, parameters of the IG distribution are estimated by maximum likelihood estimators, which are simple explicit estimators. Critical values and the actual sizes of the proposed test are obtained. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. Finally, two illustrative examples are presented and analyzed.

 The Inverse Gaussian IG distribution is widely used in practice and therefore an important issue is to develop a powerful goodness-of-fit test GOF for this distribution. In this article, we propose and examine a new GOF test for the IG distribution based on a new estimate of Kullback-Leibler KL information. The properties of the test statistic are presented. In order to compute the proposed test statistic, parameters of the IG distribution are estimated by maximum likelihood estimators, which are simple explicit estimators. Critical values and the actual sizes of the proposed test are obtained. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. Finally, two illustrative examples are presented and analyzed.

 The Inverse Gaussian IG distribution is widely used in practice and therefore an important issue is to develop a powerful goodness-of-fit test GOF for this distribution. In this article, we propose and examine a new GOF test for the IG distribution based on a new estimate of Kullback-Leibler KL information. The properties of the test statistic are presented. In order to compute the proposed test statistic, parameters of the IG distribution are estimated by maximum likelihood estimators, which are simple explicit estimators. Critical values and the actual sizes of the proposed test are obtained. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. Finally, two illustrative examples are presented and analyzed.

Makale detayları

A new goodness-of-fit test for the inverse Gaussian distribution

Yazarlar:

Hadi Alızadeh Noughabi | Mohammad Shafaei Noughabi

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics | Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.53 Issue 3, pp.823-836

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320026

pISSN:

2651-477X

Inverse Gaussian distribution

Kullback-Leibler information

Monte Carlo simulation

goodness-of-fit tests

critical points

test powerInverse Gaussian distribution

Monte Carlo simulation

Kullback-Leibler information

Goodness-of-fit tests

Critical points

Test power.

Sayı

Makale detayları

Yazarlar: Hadi Alızadeh Noughabi | Mohammad Shafaei Noughabi

Yayıncı: Hacettepe University

Ana Başlıklar: Applied Statistics | Statistics

Dergi: Hacettepe Journal of Mathematics and Statistics Vol.53 Issue 3, pp.823-836

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001320026

pISSN: 2651-477X

Sayı

Yazarlar:

Hadi Alızadeh Noughabi | Mohammad Shafaei Noughabi

Yayıncı:

Hacettepe University

Ana Başlıklar:

Applied Statistics | Statistics

Dergi:

Hacettepe Journal of Mathematics and Statistics Vol.53 Issue 3, pp.823-836

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320026

pISSN:

2651-477X