STATISTICAL INFERENCE BASED ON THE LENGTH-BIASED DATA FOR THE INVERSE GAUSSIAN DISTRIBUTION

DOI10.1080/02331889808802643zbMath0930.62020OpenAlexW2507708149WikidataQ126243144 ScholiaQ126243144MaRDI QIDQ4257228

Olcay Akman, Ramesh C. Gupta

Publication date: 21 November 1999

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889808802643

zbMATH Keywords

confidence intervalsasymptotic testsmeanreliability functioninverse Gaussian distributioncoefficient of variationlength-biased

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric hypothesis testing (62F03)

Related Items (10)

Bernstein-type inequality for weakly dependent sequence and its applications ⋮ On asymptotic approximation of inverse moments for a class of nonnegative random variables ⋮ On asymptotic approximation of ratio models for weakly dependent sequences ⋮ Recruitment and survival time under mean residual lifetime bias sampling ⋮ A note on the inverse moments for nonnegative \(\rho\)-mixing random variables ⋮ On the Crack random numbers generation procedure ⋮ Exponential inequalities and inverse moment for NOD sequence ⋮ A note on asymptotic approximations of inverse moments of nonnegative random variables ⋮ Asymptotic approximation of inverse moments of nonnegative random variables ⋮ Hypothesis testing for the population mean and variance based on r-size biased samples

Cites Work

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• What do the arithmetic, geometric and harmonic means tell us in length- biased sampling ?
• A comparison of various estimators of the mean of an inverse gaussian distribution
• Statistical Properties of Inverse Gaussian Distributions. I
• Estimation of the mean and the reciprocal of the mean of the inverse Gaussian distribution
• Survival models with two phases and length biased sampling
• A comparison between the ordinary and the length-biased modified power series distributions with applications
• Weighted Distributions and Size-Biased Sampling with Applications to Wildlife Populations and Human Families
• On the theory of screening for chronic diseases

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