### Abstract

In scenarios where the variance of a response variable can be attributed to two sources of variation, a confidence interval for a ratio of variance components gives information about the relative importance of the two sources. For example, if measurements taken from different laboratories are nine times more variable than the measurements taken from within the laboratories, then 90% of the variance in the responses is due to the variability amongst the laboratories and 10% of the variance in the responses is due to the variability within the laboratories. Assuming normally distributed sources of variation, confidence intervals for variance components are readily available. In this paper, however, simulation studies are conducted to evaluate the performance of confidence intervals under non-normal distribution assumptions. Confidence intervals based on the pivotal quantity method, fiducial inference, and the large-sample properties of the restricted maximum likelihood (REML) estimator are considered. Simulation results and an empirical example suggest that the REML-based confidence interval is favored over the other two procedures in unbalanced one-way random effects model.

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

Pages (from-to) | 3793-3807 |

Number of pages | 15 |

Journal | Journal of Statistical Planning and Inference |

Volume | 141 |

Issue number | 12 |

DOIs | |

State | Published - Dec 2011 |

### Fingerprint

### Keywords

- Fiducial inference
- Pivotal quantity
- Restricted maximum likelihood estimation

### ASJC Scopus subject areas

- Statistics, Probability and Uncertainty
- Applied Mathematics
- Statistics and Probability

### Cite this

**Confidence intervals for variance components in unbalanced one-way random effects model using non-normal distributions.** / Burch, Brent D.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Confidence intervals for variance components in unbalanced one-way random effects model using non-normal distributions

AU - Burch, Brent D

PY - 2011/12

Y1 - 2011/12

N2 - In scenarios where the variance of a response variable can be attributed to two sources of variation, a confidence interval for a ratio of variance components gives information about the relative importance of the two sources. For example, if measurements taken from different laboratories are nine times more variable than the measurements taken from within the laboratories, then 90% of the variance in the responses is due to the variability amongst the laboratories and 10% of the variance in the responses is due to the variability within the laboratories. Assuming normally distributed sources of variation, confidence intervals for variance components are readily available. In this paper, however, simulation studies are conducted to evaluate the performance of confidence intervals under non-normal distribution assumptions. Confidence intervals based on the pivotal quantity method, fiducial inference, and the large-sample properties of the restricted maximum likelihood (REML) estimator are considered. Simulation results and an empirical example suggest that the REML-based confidence interval is favored over the other two procedures in unbalanced one-way random effects model.

AB - In scenarios where the variance of a response variable can be attributed to two sources of variation, a confidence interval for a ratio of variance components gives information about the relative importance of the two sources. For example, if measurements taken from different laboratories are nine times more variable than the measurements taken from within the laboratories, then 90% of the variance in the responses is due to the variability amongst the laboratories and 10% of the variance in the responses is due to the variability within the laboratories. Assuming normally distributed sources of variation, confidence intervals for variance components are readily available. In this paper, however, simulation studies are conducted to evaluate the performance of confidence intervals under non-normal distribution assumptions. Confidence intervals based on the pivotal quantity method, fiducial inference, and the large-sample properties of the restricted maximum likelihood (REML) estimator are considered. Simulation results and an empirical example suggest that the REML-based confidence interval is favored over the other two procedures in unbalanced one-way random effects model.

KW - Fiducial inference

KW - Pivotal quantity

KW - Restricted maximum likelihood estimation

UR - http://www.scopus.com/inward/record.url?scp=79960841782&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79960841782&partnerID=8YFLogxK

U2 - 10.1016/j.jspi.2011.06.015

DO - 10.1016/j.jspi.2011.06.015

M3 - Article

AN - SCOPUS:79960841782

VL - 141

SP - 3793

EP - 3807

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 12

ER -