Results 11 to 20 of about 232,024 (305)

Pareto Type-II Model under Type-I Progressive Hybrid Censoring: Bound Lengths

Austrian Journal of Statistics, 2020
The Pareto Type-II model is considered here from which, the observable is to be predicted by using Bayesian approach. The Bayes prediction bound lengths are obtained for Type-I progressive hybrid censored data.
Gyan Prakash
doaj   +1 more source

Assessing Influence on Partially Varying-coefficient Generalized Linear Model

Revstat Statistical Journal
In this paper we discuss estimation and diagnostic procedures in partially varying-coefficient generalized linear models based in the penalized likelihood function.
Germán Ibacache-Pulgar , Valeria Lira, Cristian Villegas   +2 more
doaj   +1 more source

Bayesian Sampling Plan for Weibull Distribution with Type II Hybrid Censoring under Random Decision Function

Revstat Statistical Journal
This article studies the problem of conception of a Bayesian single variable sampling plan for Weibull distribution under type II hybrid censoring based on two-sided decision function with a linear random doubt zone.
Hacène Belbachir, Mohamed Benahmed
doaj   +1 more source

On Positive Inflated Geometric Distribution: Properties and Applications

Austrian Journal of Statistics
One-inflation in zero-truncated count data has recently found considerable attention. In this regard, zero-truncated Geometric distribution and distribution to a point mass at one are used to create a one-inflated model, namely, one-inflated zero ...
Zehra Skinder, Peer Bilal Peer, Muneeb Ahmad Wani   +2 more
doaj   +1 more source

An application of the theory of probabilities to the study of a priori pathometry.—Part I

, 1917
Prefatory .—It is somewhat surprising that so little mathematical work should have been done on the subject of epidemics, and, indeed, on the distribution of diseases in general.
R. Ross, H. P. Hudson
semanticscholar   +1 more source

Performance Assessment of Sandwich and Block Bootstrap Estimators for Temporally Dependent Bivariate Extremes

Revstat Statistical Journal
Ignoring temporal dependence when modelling sequences of extreme observations yields underestimated standard errors which can lead to inaccurate risk assessment of extreme phenomena such as floods and economic crises.
Alexander Engberg
doaj   +1 more source

Computing Quantiles in Markov Reward Models [PDF]

Foundations of Software Science and Computation Structure, 2013
Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables.
M. Ummels, C. Baier
semanticscholar   +1 more source

The quasi-xgamma frailty model with survival analysis under heterogeneity problem, validation testing, and risk analysis for emergency care data

Scientific Reports
Frailty models are important for survival data because they allow for the possibility of unobserved heterogeneity problem. The problem of heterogeneity can be existed due to a variety of factors, such as genetic predisposition, environmental factors, or ...
Hamami Loubna, Hafida Goual, Fatimah M. Alghamdi, Manahil SidAhmed Mustafa, Getachew Tekle Mekiso, M. Masoom Ali, Abdullah H. Al-Nefaie, Hassan Alsuhabi, Mohamed Ibrahim, Haitham M. Yousof   +9 more
doaj   +1 more source

Bimodal and Multimodal Extensions of the Normal and Skew Normal Distributions

Revstat Statistical Journal
A transformation of a density function is introduced to derive two families of continuous densities, the first symmetric and the second not-necessarily symmetric, exhibiting both unimodality and bimodality.
Emilio Gómez-Déniz, Enrique Calderín-Ojeda, José M. Sarabia   +2 more
doaj   +1 more source

On Hierarchical Composite Endpoints in Pediatric Cancer Supportive Care: Illustrative Examples From Two Multi‐Center Phase‐III Randomized Clinical Trials

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Pediatric supportive care clinical trials often involve multiple clinically important outcomes, complicating trial interpretation. Hierarchical composite endpoints (HCEs) provide a framework to integrate key outcomes according to clinical importance.
Willem H. Collier, Mark Zobeck, Adam J. Esbenshade, Christopher C. Dvorak, Lillian Sung, David Freyer, Sarah Alexander, Etan Orgel, Nicole J. Ullrich, Zach Prudowsky, Brian Fisher, Caitlin W. Elgarten   +11 more
wiley   +1 more source