### Abstract

Methods are presented for construction of interval estimates on the mean of a gamma distribution when there is some prior interval information as to the location of this parameter. The methods produce posterior intervals by constructing prior distributions for the mean parameter from the prior interval information. Both Bayesian and pseudo-Bayesian approaches for the construction of the priors are considered. These concepts are illustrated by an experiment assessing the operating characteristics of a laboratory chemical analyzer.

Original language | English (US) |
---|---|

Pages (from-to) | 269-273 |

Number of pages | 5 |

Journal | Technometrics |

Volume | 28 |

Issue number | 3 |

State | Published - Aug 1986 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Mathematics(all)
- Statistics and Probability

### Cite this

*Technometrics*,

*28*(3), 269-273.

**NOTE ON THE USE OF PRIOR INTERVAL INFORMATION IN CONSTRUCTING INTERVAL ESTIMATES FOR A GAMMA MEAN.** / Piegorsch, Walter W; Gladen, Beth C.

Research output: Contribution to journal › Article

*Technometrics*, vol. 28, no. 3, pp. 269-273.

}

TY - JOUR

T1 - NOTE ON THE USE OF PRIOR INTERVAL INFORMATION IN CONSTRUCTING INTERVAL ESTIMATES FOR A GAMMA MEAN.

AU - Piegorsch, Walter W

AU - Gladen, Beth C.

PY - 1986/8

Y1 - 1986/8

N2 - Methods are presented for construction of interval estimates on the mean of a gamma distribution when there is some prior interval information as to the location of this parameter. The methods produce posterior intervals by constructing prior distributions for the mean parameter from the prior interval information. Both Bayesian and pseudo-Bayesian approaches for the construction of the priors are considered. These concepts are illustrated by an experiment assessing the operating characteristics of a laboratory chemical analyzer.

AB - Methods are presented for construction of interval estimates on the mean of a gamma distribution when there is some prior interval information as to the location of this parameter. The methods produce posterior intervals by constructing prior distributions for the mean parameter from the prior interval information. Both Bayesian and pseudo-Bayesian approaches for the construction of the priors are considered. These concepts are illustrated by an experiment assessing the operating characteristics of a laboratory chemical analyzer.

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UR - http://www.scopus.com/inward/citedby.url?scp=0022760517&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0022760517

VL - 28

SP - 269

EP - 273

JO - Technometrics

JF - Technometrics

SN - 0040-1706

IS - 3

ER -