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  • Pivotal Parametric Product  (1)
  • bounded risk estimation
  • soybean
  • 2000-2004  (2)
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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Lifetime data analysis 6 (2000), S. 281-290 
    ISSN: 1572-9249
    Keywords: contamination model ; optimal tests ; Pivotal Parametric Product ; reliability distributions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Inferences on mixtures of probability distributions, in general,and of life distributions, in particular, are receiving considerableimportance in recent years. The likelihood ratio procedure oftesting for the null hypothesis of no contamination is oftenvery cumbersome and lacks its usual asymptotic properties. Recently,SenGupta (1991) has introduced the notion of an `L-optimal' testfor such testing problems. The idea is to recast the originalseveral parametric hypotheses representation of the null hypothesisin terms of only a single hypothesis involving an appropriatelychosen parametric function. This approach is shown to be bothmathematically elegant and operationally simple for a quite generalclass of mixture distributions which contains, in particular,all mixtures of the one-parameter exponential family and alsoa very rich subclass of mixtures useful in life-testing and reliabilityanalysis. It is also illustrated through two examples—onebased on real-life data and the other on a simulated sample.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1435-926X
    Keywords: Key words: Linex loss function ; bounded risk estimation ; fixed-width interval estimation ; shrinkage estimator ; asymptotic second-order expansion ; three-stage ; accelerated and purely sequential stopping rules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract. The problem of estimating a linear function of k normal means with unknown variances is considered under an asymmetric loss function such that the associated risk is bounded from above by a known quantity. In the absence of a fixed sample size rule, sequential stopping rules satisfying a general set of assumptions are considered. Two estimators are proposed and second-order asymptotic expansions of their risk functions are derived. It is shown that the usual estimator, namely the linear function of the sample means, is asymptotically inadmissible, being dominated by a shrinkage-type estimator. An example illustrates the use of different multistage sampling schemes and provides asymptotic expansions of the risk functions.
    Type of Medium: Electronic Resource
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