Article
Multidisciplinary Sciences
Manussaya La-ongkaew, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: The Weibull distribution is used to analyze data from various fields, particularly wind speed data in engineering, survival analysis, lifetime analysis, and weather forecasting. Estimating the central tendency of wind speed data accurately is important for forecasting future catastrophic events. This study compares the performance of different methods in estimating the common mean of several Weibull distributions using data from Surat Thani province, Thailand. The Bayesian highest posterior density interval is found to be the most appropriate method, providing higher coverage probabilities and shorter expected lengths compared to other methods.
Article
Mathematical & Computational Biology
Lawrence S. Segbehoe, Frank Schaarschmidt, Gemechis D. Djira
Summary: Skewed distributions and inferences concerning quantiles are common in health and social science research. Most standard simultaneous inference procedures require the normality assumption. This paper develops an asymptotic method for constructing simultaneous confidence intervals for quantiles, extending the idea to right-censored time-to-event data in survival analysis.
BIOMETRICAL JOURNAL
(2022)
Article
Mathematics
Xiao Wang, Xinmin Li
Summary: This paper investigates interval estimations for the mean of Pareto distribution with excess zeros, proposing three approaches based on fiducial generalized pivotal quantities (FGPQs). Simulation studies are conducted to evaluate the performance of the methods and compare them with other approaches, providing an analysis of their advantages and disadvantages. The methods are applied to a real phone call dataset for illustration.
Article
Mathematics
Sajid Hussain, Mahmood Ul Hassan, Muhammad Sajid Rashid, Rashid Ahmed
Summary: The study of hydrological characteristics plays a crucial role in water resources design, planning, and management. The selection of appropriate probability distributions and estimation methods are fundamental in hydrology analyses. This article proposes a new family called the 'exponentiated power alpha index generalized' (EPAIG)-G to develop various new distributions. Based on this family, a new model called the EPAIG-exponential (EPAIG-E) is developed, and its structural properties are obtained. The EPAIG-E parameters are estimated using the method of maximum likelihood (MML), and Monte Carlo simulation (MCS) is conducted to assess the model's performance using real data.
Article
Statistics & Probability
Laurent Gardes, Samuel Maistre
Summary: In this paper, new asymptotic confidence intervals for extreme quantiles are proposed. These intervals are applicable when the underlying distribution is heavy-tailed and the quantiles are located outside the range of the available data. A novel approach based on the distribution of order statistics sampled from a uniform distribution is used instead of the traditional pivotal quantity-based approach. The convergence of coverage probability to the nominal value is established under a classical second-order condition. The methodology is also applied to a real dataset to examine its performance in finite sample settings.
SCANDINAVIAN JOURNAL OF STATISTICS
(2023)
Article
Multidisciplinary Sciences
Wisunee Puggard, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: This paper investigates the methods of constructing confidence intervals for estimating the common coefficient of variation (CV) of several Birnbaum-Saunders (BS) distributions. The performances of these methods in terms of coverage probabilities and average lengths are compared. The results show that the HPDI-based confidence interval outperforms the others in all investigated scenarios.
Article
Mathematics
Weizhong Tian, Yaoting Yang, Tingting Tong
Summary: This paper studies the inferences of difference of medians for two independent log-normal distributions and discusses the simultaneous confidence intervals for more than two independent log-normal distributions. Simulation studies show that the parametric bootstrap approach is suitable for smaller sample sizes, while the method of variance estimates recovery and normal approximation approaches are alternatives, especially when the populations have large variance.
Article
Business, Finance
Yaacov Kopeliovich, Kevin Shea
Summary: We derive a formula to compute confidence intervals for stress testing predictions and compare it with a hypothetical scenario bootstrap methodology through numerical examples.
FINANCE RESEARCH LETTERS
(2023)
Article
Multidisciplinary Sciences
Wisunee Puggard, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: This study proposes several methods for calculating confidence intervals for the ratio of variances of two independent samples based on asymmetric distribution. Through Monte Carlo simulation, it is found that the HPD-PI method performs well for all sample sizes. The efficacy of these methods is also demonstrated by applying them to real datasets.
Article
Statistics & Probability
Jesse Frey, Yimin Zhang
Summary: Melded confidence intervals are proposed to combine two one-sample confidence intervals, but they do not guarantee the nominal coverage when calculating the difference in population quantiles.
AMERICAN STATISTICIAN
(2023)
Article
Multidisciplinary Sciences
Wisunee Puggard, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: This study introduces four methods for constructing confidence intervals for the coefficient of variation (CV) and the difference between CVs of Birnbaum-Saunders (BS) distributions. A Monte Carlo simulation shows that the highest posterior density (HPD) interval performs best overall. The proposed methods were validated using PM 2.5 concentration data for Chiang Mai, Thailand in March and April 2019, with results consistent with the simulation findings.
Article
Mathematics
Theerapong Kaewprasert, Sa-Aat Niwitpong, Suparat Niwitpong
Summary: Heavy rain in September can cause flooding and other natural disasters in many areas of Thailand. We propose six methods for constructing simultaneous confidence intervals and evaluate their performance using Monte Carlo simulation. The results show that the HPD interval based on the Jeffreys'rule prior performs the best in most cases.
Article
Computer Science, Interdisciplinary Applications
Ignacio Erazo, David Goldsman
Summary: This study investigates the properties of confidence intervals (CIs) for the difference of success parameters in two Bernoulli distributions. The findings reveal that, for multi-stage methods, a simple observation allocation rule based on comparing the sample standard deviations of the two populations is more efficient, and the moderate use of batching can save stages at only modest costs in terms of sample size and coverage.
JOURNAL OF SIMULATION
(2023)
Article
Engineering, Industrial
Baocai Guo, Qiming Xia, Yingying Sun, Muhammad Aslam
Summary: This article constructs generalized confidence intervals for two commonly used PCIs based on the inverse Gaussian distribution and considers the confidence interval for the difference between two inverse Gaussian processes' PCIs. The simulation results demonstrate that the proposed confidence interval outperforms the traditional bootstrap method in terms of coverage probabilities.
QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT
(2023)
Article
Statistics & Probability
Suthakaran Ratnasingam, Spencer Wallace, Imran Amani, Jade Romero
Summary: This article introduces three modified empirical likelihood approaches to construct confidence intervals for the generalized Lorenz ordinate. The proposed methods are validated through simulation and real data.
COMPUTATIONAL STATISTICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)