Shrinkage estimation in exponential type-II censored data under LINEX loss

Prakash, Gyan; Singh, D.C.

doi:10.1016/j.jkss.2007.07.002

Cited by 30 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• If H 0 : θ = θ 0 is accepted, then the inequality q 1 ≤ 2n ≤ q 2 (Prakash and Singh , 2008) implies that q 1 /(2n) ≤ 1. For small values of shrinkage factor, we can take q 1 /(2n) ≈ 1.…”

Section: Shrinkage Preliminary Test Estimatorsmentioning

confidence: 99%

“…One can construct shrinkage preliminary test estimators for the parameter θ based on the acceptance or rejection of H 0 . Pandey and Singh (1980), Prakash and Singh (2008), Kibria et al (2010), Ahmed et al (2012), Mirfarah and Ahmadi (2014), Arabi Belaghi et al (2014, 2015a, Naghizadeh Qomi and Barmoodeh (2015) and Hossain and Howlader (2016) considered the problem of shrinkage estimation. The aim of this paper is constructing shrinkage preliminary test estimators in exponential distribution under a precautionary loss function.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

Kiapour

Qomi

2016

JIRSS

View full text Add to dashboard Cite

Abstract. Shrinkage preliminary test estimation in exponential distribution under a precautionary loss function is considered. The minimum risk-unbiased estimator is derived and some shrinkage preliminary test estimators are proposed. We apply our results on censored data and records. The relative efficiencies of proposed estimators with respect to the minimum risk-unbiased estimator based on record data under the considered loss function are computed for evaluating the performance of these estimators.

show abstract

Section: Shrinkage Preliminary Test Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

Kiapour

Qomi

2016

JIRSS

View full text Add to dashboard Cite

show abstract

“…If H 0 : θ = θ 0 is accepted, then following Prakash and Singh (2008), the inequality q 1 ⩽ 2mT m /θ 0 ⩽ q 2 implies that q 1 ⩽ 2m ⩽ q 2 and then q 1 /(2m) ⩽ 1. For small values of shrinkage factor, we can take q 1 /(2m) ≈ 1.…”

Section: Shrinkage Testimatorθ (3) Stmentioning

confidence: 99%

Shrinkage Testimation in Exponential Distribution based on Records under Asymmetric Squared Log Error Loss

Qomi¹,

Barmoodeh²

2016

JSRI

View full text Add to dashboard Cite

Abstract. In the present paper, we study shrinkage testimation for the unknown scale parameter θ > 0 of the exponential distribution based on record data under the asymmetric squared log error loss function. A minimum risk unbiased estimator within the class of the estimators of the form cT m is derived, where T m is the maximum likelihood estimate of θ. Some shrinkage testimators are proposed and their risks are computed. The relative efficiencies of the shrinkage testimators with respect to a minimum risk unbiased estimator of the form cT m under the squared log error loss function are calculated for the comparison purposes. An illustrative example is also presented.

show abstract

“…The shrinkage estimators have been discussed by a number of others, for details see Lehmann and Casella (1998), Prakash and Singh (2006, 2008, 2009). The preliminary test estimator given in Section Among various kinds of shrinkage estimators proposed so far, Jani (1991) and Kourouklis (1994) have suggested shrinkage estimators for the scale parameter in one and two-parameter exponential distributions.…”

Section: Shrinkage Estimatormentioning

confidence: 99%

Preliminary Test and ‎‎Shrinkage Estimations of Scale Parameters for Two Exponential Distributions based on Record Values

Zakerzadeh¹,

Jafari²,

Karimi³

2016

JSRI

View full text Add to dashboard Cite

The exponential distribution is applied in a very wide variety of statistical procedures.Among the most prominent applications are those in the field of life testing and reliability theory. When there are two record samples available for estimating the scale parameter, a preliminary test is usually used to determine whether to pool the samples or use the individual sample. In this paper, the preliminary test estimator and shrinkage estimator are studied. The optimum level of significance for preliminary test estimation and the optimum values of shrinkage coefficient are obtained based on minimax regret criterion under the weighted square error loss function.

show abstract

Shrinkage estimation in exponential type-II censored data under LINEX loss

Abstract: This paper deals with the study of the performance of the shrinkage testimators under the invariant version of LINEX loss function for the scale parameter of an exponential distribution when type-II censored data are available.

Cited by 30 publications

References 15 publications

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

Shrinkage preliminary test estimation under a precautionary loss function with applications on records and censored ‎data

Shrinkage Testimation in Exponential Distribution based on Records under Asymmetric Squared Log Error Loss

Preliminary Test and ‎‎Shrinkage Estimations of Scale Parameters for Two Exponential Distributions based on Record Values

Contact Info

Product

Resources

About