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Antonio Sanhueza
Researcher at University of La Frontera
Publications - 77
Citations - 1988
Antonio Sanhueza is an academic researcher from University of La Frontera. The author has contributed to research in topics: Birnbaum–Saunders distribution & Population. The author has an hindex of 23, co-authored 69 publications receiving 1834 citations. Previous affiliations of Antonio Sanhueza include Autonomous University of Chile & University of North Carolina at Chapel Hill.
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Effects of a tailored health promotion program for female blue-collar workers: health works for women.
Marci K. Campbell,Irene Tessaro,Brenda M. DeVellis,Salli Benedict,Kristine Kelsey,Leigh Belton,Antonio Sanhueza +6 more
TL;DR: The HWW project was a successful model for achieving certain health behavior changes among blue-collar women and demonstrated improvements in strengthening and flexibility exercise compared to the delayed group.
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An R Package for a General Class of Inverse Gaussian Distributions
TL;DR: The ig package as mentioned in this paper is designed to analyze data from inverse Gaussian type distributions, which contains basic probabilistic functions, lifetime indicators and a random number generator from this model.
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The Generalized Birnbaum–Saunders Distribution and Its Theory, Methodology, and Application
TL;DR: The generalized Birnbaum-saunders distribution as mentioned in this paper is a family of distributions suitable for modeling lifetime data as it allows for different degrees of kurtosis and asymmetry and unimodality as well as bimodality.
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Generalized Birnbaum-Saunders distributions applied to air pollutant concentration
TL;DR: The generalized Birnbaum-saunders (GBS) distribution is a new class of positively skewed models with lighter and heavier tails than the traditional Birnba-Saunders distribution, which is largely applied to study lifetimes.
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Lifetime analysis based on the generalized Birnbaum-Saunders distribution
TL;DR: This paper considers a family of generalized Birnbaum-Saunders distributions and presents a lifetime analysis based mainly on the hazard function of this model, and carries out maximum likelihood estimation by using an iterative algorithm, which produces robust estimates.