scholarly journals Generalized Inference on Stress-Strength Reliability in Generalized Pareto Model

2021 ◽  
Author(s):  
Sanju Scaria ◽  
Seemon Thomas ◽  
Sibil Jose
Keyword(s):  
Simulation Studies ◽  
Strength Data ◽  
Percentile Method ◽  
Generalized Pareto ◽  
Variable Approach ◽  
Pareto Model ◽  
Coverage Probabilities ◽  
Generalized Inference ◽  
Bootstrap Percentile ◽  
Generalized Variable Approach

The article focuses on the inference of stress-strength reliability in generalized Pareto model using the generalized variable approach and bootstrap percentile method. Simulation studies are conducted to obtain expected lengths and coverage probabilities of confidence intervals constructed using the generalized variable and the bootstrap percentile methods. An example consisting of real stress-strength data is also presented for illustrative purposes.

A Note on Bootstrapping Generalized Method of Moments Estimators

1996 ◽  
Vol 12 (1) ◽  
pp. 187-197 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
New York ◽  
Limit Distribution ◽  
Generalized Method Of Moments ◽  
Test Statistic ◽  
Percentile Method ◽  
Coverage Probabilities ◽  
Bootstrap Percentile ◽  
Nominal Coverage ◽  
Moments Estimators ◽  
Bootstrap Distribution

Recently, Arcones and Giné (1992, pp. 13–47, in R. LePage & L. Billard [eds.], Exploring the Limits of Bootstrap, New York: Wiley) established that the bootstrap distribution of the M-estimator converges weakly to the limit distribution of the estimator in probability. In contrast, Brown and Newey (1992, Bootstrapping for GMM, Seminar note) discovered that the bootstrap distribution of the GMM overidentification test statistic does not converge weakly to the x2 distribution. In this paper, it is shown that the bootstrap distribution of the GMM estimator converges weakly to the limit distribution of the estimator in probability. Asymptotic coverage probabilities of the confidence intervals based on the bootstrap percentile method are thus equal to their nominal coverage probability.

Bootstrapping Quantile Regression Estimators

1995 ◽  
Vol 11 (1) ◽  
pp. 105-121 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
Quantile Regression ◽  
Limit Distribution ◽  
Asymptotic Variance ◽  
Regression Estimator ◽  
Variance Matrix ◽  
Percentile Method ◽  
Coverage Probabilities ◽  
Regression Estimators ◽  
Bootstrap Percentile ◽  
Bootstrap Distribution

The asymptotic variance matrix of the quantile regression estimator depends on the density of the error. For both deterministic and random regressors, the bootstrap distribution is shown to converge weakly to the limit distribution of the quantile regression estimator in probability. Thus, the confidence intervals constructed by the bootstrap percentile method have asymptotically correct coverage probabilities.

scholarly journals The Gamma Generalized Pareto Distribution with Applications in Survival Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 141 ◽  
Author(s):  
Thiago A. N. De Andrade ◽  
Luz Milena Zea Fernandez ◽  
Frank Gomes-Silva ◽  
Gauss M. Cordeiro
Keyword(s):  
Monte Carlo Simulation ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Real Data ◽  
Model Parameters ◽  
Monte Carlo Simulation Study ◽  
Lorenz Curves ◽  
Generalized Pareto ◽  
Mathematical Properties ◽  
Pareto Model

We study a three-parameter model named the gamma generalized Pareto distribution. This distribution extends the generalized Pareto model, which has many applications in areas such as insurance, reliability, finance and many others. We derive some of its characterizations and mathematical properties including explicit expressions for the density and quantile functions, ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating function, R\'enyi entropy and order statistics. We discuss the estimation of the model parameters by maximum likelihood. A small Monte Carlo simulation study and two applications to real data are presented. We hope that this distribution may be useful for modeling survival and reliability data.

Modeling the Seasonality of Extreme Waves in the Gulf of Mexico

2010 ◽  
Vol 133 (2) ◽  
Author(s):  
Philip Jonathan ◽  
Kevin Ewans
Keyword(s):  
Gulf Of Mexico ◽  
Wave Height ◽  
Significant Wave Height ◽  
Poisson Model ◽  
Extreme Value ◽  
Significant Wave ◽  
Generalized Pareto ◽  
Seasonal Model ◽  
Pareto Model ◽  
Optimal Value

Statistics of storm peaks over threshold depend typically on a number of covariates including location, season, and storm direction. Here, a nonhomogeneous Poisson model is adopted to characterize storm peak events with respect to season for two Gulf of Mexico locations. The behavior of storm peak significant wave height over threshold is characterized using a generalized Pareto model, the parameters of which vary smoothly with season using a Fourier form. The rate of occurrence of storm peaks is also modeled using a Poisson model with rate varying with season. A seasonally varying extreme value threshold is estimated independently. The degree of smoothness of extreme value shape and scale and the Poisson rate with season are regulated by roughness-penalized maximum likelihood; the optimal value of roughness is selected by cross validation. Despite the fact that only the peak significant wave height event for each storm is used for modeling, the influence of the whole period of a storm on design extremes for any seasonal interval is modeled using the concept of storm dissipation, providing a consistent means to estimate design criteria for arbitrary seasonal intervals. The characteristics of the 100 year storm peak significant wave height, estimated using the seasonal model, are examined and compared with those estimated ignoring seasonality.

scholarly journals Methods for Exploring Spatial and Temporal Variability of Extreme Events in Climate Data

2008 ◽  
Vol 21 (10) ◽  
pp. 2072-2092 ◽  
Author(s):  
C. A. S. Coelho ◽  
C. A. T. Ferro ◽  
D. B. Stephenson ◽  
D. J. Steinskog
Keyword(s):  
Value Theory ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Spatial Dependency ◽  
Climate Data ◽  
Explanatory Factors ◽  
Generalized Pareto ◽  
Pareto Model ◽  
Grid Points ◽  
Extremal Properties

Abstract This study presents various statistical methods for exploring and summarizing spatial extremal properties in large gridpoint datasets. Extremal properties are inferred from the subset of gridpoint values that exceed sufficiently high, time-varying thresholds. A simple approach is presented for how to choose the thresholds so as to avoid sampling biases from nonstationary differential trends within the annual cycle. The excesses are summarized by estimating parameters of a flexible generalized Pareto model that can account for spatial and temporal variation in the excess distributions. The effect of potentially explanatory factors (e.g., ENSO) on the distribution of extremes can be easily investigated using this model. Smooth spatially pooled estimates are obtained by fitting the model over neighboring grid points while accounting for possible spatial variation across these points. Extreme value theory methods are also presented for how to investigate the temporal clustering and spatial dependency (teleconnections) of extremes. The methods are illustrated using Northern Hemisphere monthly mean gridded temperatures for June–August (JJA) summers from 1870 to 2005.

Confidence Intervals for the Ratio of the Coefficients of Variation of Inverse-Gamma Distributions

2021 ◽  
Author(s):  
Theerapong Kaewprasert ◽  
Sa-Aat Niwitpong ◽  
Suparat Niwitpong
Keyword(s):  
Confidence Intervals ◽  
Bayesian Method ◽  
Simulation Studies ◽  
Jeffreys Prior ◽  
Inverse Gamma ◽  
Gamma Distributions ◽  
Coefficients Of Variation ◽  
Coverage Probabilities ◽  
Percentile Bootstrap ◽  
Better Than

Herein, we present four methods for constructing confidence intervals for the ratio of the coefficients of variation of inverse-gamma distributions using the percentile bootstrap, fiducial quantities, and Bayesian methods based on the Jeffreys and uniform priors. We compared their performances using coverage probabilities and expected lengths via simulation studies. The results show that the confidence intervals constructed with the Bayesian method based on the uniform prior and fiducial quantities performed better than those constructed with the Bayesian method based on the Jeffreys prior and the percentile bootstrap. Rainfall data from Thailand was used to illustrate the efficacies of the proposed methods.

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