On a two stage shrinkage testimator of the mean of a normal distribution

Waikar, V. B.; Schuurmann, F. J.; Raghunathan, T. E.

doi:10.1080/03610928408828802

Cited by 36 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a so-called preliminary test method, similar in spirit to testimators that are sometimes used in econometrics (e.g. Waikar et al (1984)). Methods of this type have been shown to be inferior in both accuracy and calibration to random effects methods, such as the empirical Bayes approach mentioned above, that deal more smoothly with the uncertainty about r (see, for example, Sclove et al (1972) and Greenland (1993)).…”

Section: Computation and Calibration Issuesmentioning

confidence: 99%

Assessment and Propagation of Model Uncertainty

Draper

1995

Journal of the Royal Statistical Society Series B: Statistical Methodology

1,139

828

View full text Add to dashboard Cite

SUMMARY In most examples of inference and prediction, the expression of uncertainty about unknown quantities y on the basis of known quantities x is based on a model M that formalizes assumptions about how x and y are related. M will typically have two parts: structural assumptions S, such as the form of the link function and the choice of error distribution in a generalized linear model, and parameters θ whose meaning is specific to a given choice of S. It is common in statistical theory and practice to acknowledge parametric uncertainty about θ given a particular assumed structure S; it is less common to acknowledge structural uncertainty about S itself. A widely used approach involves enlisting the aid of x to specify a plausible single ‘best’ choice S* for S, and then proceeding as if S* were known to be correct. In general this approach fails to assess and propagate structural uncertainty fully and may lead to miscalibrated uncertainty assessments about y given x. When miscalibration occurs it will often result in understatement of inferential or predictive uncertainty about y, leading to inaccurate scientific summaries and overconfident decisions that do not incorporate sufficient hedging against uncertainty. In this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the US space shuttle.

show abstract

Section: Computation and Calibration Issuesmentioning

confidence: 99%

Assessment and Propagation of Model Uncertainty

Draper

1995

Journal of the Royal Statistical Society Series B: Statistical Methodology

1,139

828

View full text Add to dashboard Cite

show abstract

“….e −w w r −1 dw and y may be a function of w. Waikar, Schuurmann, and Raghunathan (1984) has suggested an idea of selecting the shrinkage factor which is the function of the test statistic i.e., under…”

Section: Shrinkage Testimators and Their Propertiesmentioning

confidence: 99%

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

Prakash

Singh

2008

Journal of the Korean Statistical Society

View full text Add to dashboard Cite

show abstract

“…• If H 0 : θ = θ 0 is accepted, then the inequality q 1 ≤ 2Y n /θ 0 ≤ q 2 (Waikar et al , 1984) implies that…”

Section: Shrinkage Preliminary Test Estimatorsmentioning

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

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

On a two stage shrinkage testimator of the mean of a normal distribution

Cited by 36 publications

References 5 publications

Assessment and Propagation of Model Uncertainty

Assessment and Propagation of Model Uncertainty

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

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

Contact Info

Product

Resources

About