Article
Mathematics, Applied
Hassan Okasha, Mazen Nassar, Saeed A. Dobbah
Summary: This paper investigates the E-Bayesian estimation of the parameter and reliability function of the Burr type-XII distribution under adaptive progressive Type-II censoring scheme, using three different prior distributions and discussing properties under squared error and LINEX loss functions. An extensive simulation study compares the E-Bayesian estimation with Bayes and maximum likelihood estimators, and a real data set analysis demonstrates the applicability of different estimators in practice.
Article
Mathematics
Xuanjia Zuo, Liang Wang, Huizhong Lin, Sanku Dey, Li Yan
Summary: This paper focuses on estimating Weibull products using generalized progressive hybrid censoring, with classical and Bayesian inferences considered for unknown parameters. Extensive numerical analysis shows the superiority of the proposed approaches in practical lifetime experiments.
JOURNAL OF MATHEMATICS
(2021)
Article
Mathematics
Gabriela M. Rodrigues, Edwin M. M. Ortega, Gauss M. Cordeiro, Roberto Vila
Summary: This study examines the factors that increase the risk of death in hospitalized patients with COVID-19 using the odd log-logistic regression model and provides new mathematical properties of this distribution. The simulation results demonstrate the consistency of the estimates and suggest that the proposed model is efficient in identifying the determinant variables for individual survival.
Article
Mathematics, Applied
Yuge Du, Wenhao Gui
Summary: This paper discusses the adaptive type II progressive censored data under the competitive risk model from multiple aspects such as experimental method comparison, data analysis, and optimized censoring scheme. The existence and uniqueness of the maximum likelihood estimation are derived, and the approximate confidence interval is constructed by the Fisher information matrix and delta method. Bayesian estimation under three loss functions and the highest posterior density credible intervals are provided via Markov Chain Monte Carlo simulations. Considering the effect of optimized censoring schemes to improve the efficiency of experiments, three optimization criteria are introduced under the condition of ensuring the amount of data and shortening the test duration. Finally, suggestions for experimental design are presented to better serve the actual production and life.
RESULTS IN MATHEMATICS
(2022)
Article
Engineering, Multidisciplinary
Javeria Khaleeq, Muhammad Amanullah, Alanazi Talal Abdulrahman, E. H. Hafez, M. M. Abd El-Raouf
Summary: The study focused on diagnostic methods for identifying unusual observations in the Log-Logistic regression model, proposing a new approach based on local influence diagnostics, and demonstrating its superiority through an illustrative example and simulation study.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Computer Science, Software Engineering
Cagatay Cetinkaya
Summary: This paper investigates the estimation of R=P(X>Y)$$ R=P\left(X>Y\right) $$ based on the Burr-XII distribution under the generalized progressive hybrid censoring scheme. The inferences of R$$ R $$ are obtained using maximum likelihood and Bayesian estimation methods. Simulation studies evaluate the performance of the proposed estimators, and real-data examples illustrate the theoretical outcomes.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Mathematics
Anwaar Dhiaa Abdulkareem, Sunbul Rasheed Mohammed
Summary: This article presents a comparison between two new censored regression models, with LBXIIW model found to be better than LBXIIEE model in the experiment.
INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaoyu Liu, Liming Xiang
Summary: This study proposes a more flexible class of generalized accelerated hazards mixture cure models for analyzing interval-censored failure times. A sieve maximum likelihood estimation method is used to approximate the unknown cumulative baseline hazard function with B-splines. Simulation results demonstrate satisfactory performance of the proposed method in finite samples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Mathematics, Applied
Shikhar Tyagi, Arvind Pandey, Varun Agiwal, Christophe Chesneau
Summary: The article introduces a method based on frailty models to study the impact of unobserved covariates, using generalized Weibull and generalized log-logistic-II distributions as baseline distributions, and employing Bayesian methods to estimate model parameters and perform model comparisons, with results showing that the new models perform better.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Statistics & Probability
Soheila Akbari Bargoshadi, Hossein Bevrani, Reza Arabi Belaghi
Summary: In this article, statistical inferences for the Burr-XII distribution under a joint type-II censoring scheme are discussed. Classical likelihood estimation and Bayesian estimations with a gamma prior distribution are studied. The performance of the methods is investigated through simulations and real-life examples. Results suggest that Bayesian approaches outperform the EM algorithm in terms of mean square error and coverage probability.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Statistics & Probability
Valdemiro P. Vigas, Edwin M. M. Ortega, Adriano K. Suzuki, Gauss M. Cordeiro, Paulo C. dos Santos Junior
Summary: The article introduces a new regression method based on the generalized odd log-logistic family for interval-censored data. This family is suitable for interval modeling as it generalizes popular lifetime distributions and can represent various forms of the risk function. The parameters are estimated using classical and Bayesian methods, and the goodness of fit is assessed using selection criteria, likelihood ratio tests, residual analysis, and graphical techniques. Two real data sets demonstrate the usefulness of the proposed models.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Mathematics
Tahani A. Abushal, A. A. Soliman, G. A. Abd-Elmougod
Summary: This paper addresses the problem of statistical inference under joint censoring samples using the competing risks model. Maximum likelihood and Bayes estimators are obtained, and interval estimation is discussed through various methods such as asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. Numerical results are discussed and summarized through real data analysis and Monte Carlo simulation study, with key points listed in a brief comment.
JOURNAL OF MATHEMATICS
(2021)
Article
Mathematics, Applied
Fernando Jose Monteiro de Araujo, Renata Rojas Guerra, Fernando A. Pena-Ramirez
Summary: This paper proposes a quantile regression model based on a new parameterization of the Burr XII distribution, suitable for modeling asymmetric data with heavy tails. The model parameters are estimated using the maximum-likelihood method and their performance is evaluated through a Monte Carlo simulation study. Diagnostic tools and selection criteria for the new regression model are also presented. An empirical application to the salaries of players in the Western division of the American League of the Major League Baseball is discussed to illustrate the usefulness of the model.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Debajyoti Sinha, Piyali Basak, Stuart R. Lipsitz
Summary: This article investigates the impact of robotic surgery on the time to Prostate Specific Antigen (PSA) recurrence. Three competing novel models are proposed and analyzed using frequentist and Bayesian methods. Simulation studies show that the second model is highly robust.
LIFETIME DATA ANALYSIS
(2022)
Article
Economics
Naveen Narisetty, Roger Koenker
Summary: A new quantile regression model for survival data is proposed, allowing some subjects to become unsusceptible to disease recurrence after treatment. The new approach of data augmentation estimation has computational advantages over prior methods, demonstrating advantageous empirical performance in simulations. The proposed method is compared against existing strategies and illustrated with data from a Lung Cancer survival study.
JOURNAL OF ECONOMETRICS
(2022)
Article
Statistics & Probability
Fabio Prataviera, Edwin M. M. Ortega, Gauss M. Cordeiro
Summary: We propose a semiparametric regression approach based on the generalized odd log-logistic Maxwell distribution, utilizing cubic spline with linear and nonlinear effects for modeling censored and uncensored data. The parameter estimates are determined using penalized likelihood. New standards for global influence diagnostics and quantile residuals are addressed. Monte Carlo simulations are conducted to investigate the consistency of estimates. The usefulness of the new regression is demonstrated through applications to real data.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
(2023)
Article
Statistics & Probability
E. M. Hashimoto, E. M. M. Ortega, G. M. Cordeiro, V. G. Cancho, I Silva
Summary: This paper re-parameterizes the inverse Gaussian distribution to establish an association between a linear predictor and the variance, and proposes deviance residuals to verify model assumptions. Through simulations and analysis of real data, it is demonstrated that the re-parameterized inverse Gaussian model is a viable choice for analyzing censored data with non-constant variance.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Statistics & Probability
J. C. S. Vasconcelos, G. M. Cordeiro, E. M. M. Ortega, G. O. Silva
Summary: This paper proposes a regression model with random effect at the intercept based on the generalized inverse Gaussian distribution model for analyzing correlated data. The versatility of the model is demonstrated by estimating the average price per hectare of bare land in municipalities in Sao Paulo, Brazil.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Statistics & Probability
Fabio Prataviera, Roberto Vila, Vicente G. Cancho, Edwin M. M. Ortega, Gauss M. Cordeiro
Summary: We propose a reparameterized regression for estimating the median of an extended Maxwell distribution with positive support. We investigate the structural properties of the distribution and estimate the parameters using maximum likelihood and Bayesian methods. Influence measures and quantile residuals are defined, and Monte Carlo simulations are conducted for inference purposes. The new regression is also applied to an experimental data set.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Statistics & Probability
Vicente G. Cancho, Elizbeth C. Bedia, Gauss M. Cordeiro, Fabio Prataviera, Edwin M. M. Ortega, Ana P. J. E. Santo
Summary: A new survival model is proposed in this paper to consider the presence of surviving fractions and unobserved dispersion. The model is obtained by considering multiple latent factors that generate the observed lifetime according to a generalized Poisson distribution, including the promotion time cure model as a special case. Maximum likelihood tools and the expectation maximization algorithm are used for inference and parameter estimation, while the likelihood ratio test is employed for model discrimination. The new regression model is applied to cervical cancer data to evaluate the effects of covariates on the cured fraction and non-cured group.
COMPUTATIONAL STATISTICS
(2023)
Article
Public, Environmental & Occupational Health
Audencio Victor, Rita de Cassia Ribeiro Silva, Natanael de Jesus Silva, Andrea Ferreira, Mauricio L. Barreto, Tereza Campello
Summary: This study aims to explore the association between unhealthy food environments and premature cardiovascular disease mortality in the Brazilian population. The findings show that municipalities with a greater offer of ultraprocessed foods have a higher risk of death from cardiovascular diseases, stroke, and ischemic heart disease. Thus, initiatives to minimize the effects of these food environments are urgently needed in Brazil.
AMERICAN JOURNAL OF PREVENTIVE MEDICINE
(2023)
Article
Statistics & Probability
F. Prataviera, E. M. Hashimoto, E. M. M. Ortega, G. M. Cordeiro, V. G. Cancho, R. Vila
Summary: The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and recovery probabilities of Chinese individuals with COVID-19. Regression coefficients and recovered fraction are estimated using maximum likelihood and Bayesian methods. Simulation studies are conducted to compare regressions for different scenarios. The findings suggest that the proposed model could be a good alternative to analyze censored lifetime of individuals with COVID-19.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Public, Environmental & Occupational Health
Lorena Suarez-Idueta, Robespierre Pita, Hannah Blencowe, Arturo Barranco, Jesus F. Gonzalez, Enny S. Paixao, Mauricio L. Barreto, Joy E. Lawn, Eric O. Ohuma
Summary: This study linked administrative databases of live births and under-five child deaths in Mexico to explore mortality and trends for preterm, SGA, and LGA children. The results showed that neonatal mortality rate was higher in preterm infants compared to term infants, SGA children had a higher mortality rate compared to AGA children, infants born at <28 weeks had the highest mortality rate, and LGA children had no additional risk compared to AGA children. This study demonstrated the importance of linked data in understanding neonatal vulnerability and child mortality, and provided a valuable resource for future population-based research.
PAEDIATRIC AND PERINATAL EPIDEMIOLOGY
(2023)
Article
Medicine, General & Internal
Maria da Conceicao N. Costa, Luciana Lobato L. Cardim, Cynthia C. Moore, Eliene dos Santos de Jesus, Rita Carvalho-Sauer, Mauricio B. Barreto, Laura K. Rodrigues, Liam Smeeth, Lavinia Schuler-Faccini, Elizabeth Brickley, Wanderson Oliveira, Eduardo Hage V. Carmo, Julia Moreira C. Pescarini, Roberto F. S. Andrade, Moreno M. S. Rodrigues, Rafael S. Veiga, Larissa Costa, Giovanny V. A. Franca, Maria Gloria Teixeira, Enny Paixao
Summary: This study aimed to describe the sequence of events leading to death of children with congenital Zika syndrome (CZS) up to 36 months of age and their probability of dying from a given cause, 2015 to 2018. The study found that CZS children's deaths were mainly due to multiple congenital malformations not classified elsewhere and unspecified septicemia, indicating their greater vulnerability to infectious and respiratory conditions compared to children with non-Zika-related CNS congenital anomalies.
Article
Medicine, General & Internal
Enny C. Paixao, Andrea J. F. Ferreira, Idalia Oliveira dos Santos, Laura Rodrigues, Rosemeire Fiaccone, Leonardo Salvi, Guilherme Lopes de Oliveira, Jose Guilherme Santana, Andrey Moreira Cardoso, Carlos Antonio de S. S. Teles, Maria Auxiliadora L. Soares, Eliana Amaral, Liam Smeeth, Mauricio Barreto, Maria Yury Ichihara, Philippa Dodd
Summary: This study aimed to estimate excess all-cause mortality in children under 5 years with congenital syphilis (CS) compared to those without CS. The findings showed that children with CS had a significantly higher mortality rate than those without CS. Therefore, timely detection and treatment of pregnant women with CS can reduce vertical transmission and mitigate child mortality.
Article
Green & Sustainable Science & Technology
Gervasio F. dos Santos, Alejandra Vives Vergara, Mauricio Fuentes-Alburquenque, Jose Firmino de Sousa Filho, Aureliano Sancho Paiva, Andres Felipe Useche, Goro Yamada, Tania Alfaro, Amelia A. Lima Friche, Roberto F. S. Andrade, Mauricio L. Barreto, Waleska Teixeira Caiaffa, Ana V. Diez-Roux
Summary: This study aims to identify typologies of Latin American cities based on socioeconomic urban environment patterns. Census data from 371 urban agglomerations in 11 countries were used to identify socioeconomic typologies of cities in Latin America. Five socioeconomic regional typology patterns were identified, including low-education cities in Northeast Brazil, low-unemployment cities in Peru and Panama, high-education cities in Argentina, Chile, Colombia, Costa Rica, Nicaragua and Mexico, high female labor participation with high primary education in Argentina and low primary education in Brazil, and low female labor participation and low education in Brazil, Colombia, El Salvador, Guatemala, and Mexico.
Review
Allergy
Philip J. Cooper, Camila A. Figueiredo, Alejandro Rodriguez, Leticia Marques dos Santos, Rita C. Ribeiro-Silva, Valdirene Leao Carneiro, Gustavo Costa, Thiago Magalhaes, Talita dos Santos de Jesus, Raimon Rios, Hugo Bernardino F. da Silva, Ryan Costa, Martha E. Chico, Maritza Vaca, Neuza Alcantara-Neves, Laura C. Rodrigues, Alvaro A. Cruz, Mauricio L. Barreto
Summary: Asthma in Latin America (LA) shows variable prevalence and disease burden between countries, with high prevalence and morbidity in marginalized urban populations. Research has shown that childhood asthma in LA is primarily non-atopic and is associated with environmental and lifestyle factors, such as poor living conditions and respiratory infections. Genetic factors, particularly African ancestry, increase asthma risk in LA settings. Access to healthcare and medication is crucial for controlling asthma in LA. Future research should focus on identifying relevant endotypes and underlying causes, with a particular emphasis on implementing strategies in resource-poor settings.
CLINICAL AND TRANSLATIONAL ALLERGY
(2023)
Article
Multidisciplinary Sciences
Raimon Rios, Thiago Magalhaes da Silva, Agostino Strina, Erick Forno, Ryan Costa, Juan C. Celedon, Mauricio L. Barreto, Camila Alexandrina Figueiredo
Summary: Genetic variants in filaggrin (FLG) are associated with eczema, and the association is modified by African ancestry. The T allele of SNP rs6587666 in FLG is negatively associated with eczema, and this association is influenced by the degree of African ancestry.
Article
Public, Environmental & Occupational Health
Qeren Hapuk R. Ferreira Fernandes, Enny S. Paixao, Maria da Conceicao N. Costa, Maria Gloria Teixeira, Juliana Darbra Cruz Rios, Keila da Silva Goes Di Santo, Mauricio L. Barreto, Angelina Xavier Acosta
Summary: Congenital anomalies are a significant issue for global public health, affecting approximately 3% to 6% of newborns worldwide. In Brazil, they are the second leading cause of infant mortality. This study examines the prevalence and infant mortality trends of congenital anomalies in Brazil and regions from 2001 to 2018, using data from the Live Birth Information System and the Mortality Information System. The study finds an increasing prevalence and infant mortality rate of congenital anomalies in Brazil, particularly in the Northeast and North regions.
CIENCIA & SAUDE COLETIVA
(2023)
Article
Physics, Multidisciplinary
Mohamed Hussein, Gabriela M. Rodrigues, Edwin M. M. Ortega, Roberto Vila, Howaida Elsayed
Summary: We present the truncated Lindley-G (TLG) model, which is a new class of probability distributions with an additional shape parameter. The characteristics of the proposed model, including critical points, moments, generating function, and quantile function, are discussed. We also introduce a regression model based on the truncated Lindley-Weibull distribution and estimate the model parameters using the maximum likelihood method. Simulations and real data analysis demonstrate the potential of the new model.
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)
