Computational Statistics最新文献_第10页

Analysis of estimating the Bayes rule for Gaussian mixture models with a specified missing-data mechanism 对具有指定缺失数据机制的高斯混合物模型贝叶斯规则的估计分析

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-10 DOI: 10.1007/s00180-023-01447-0

引用次数: 0

Finite mixture of regression models for censored data based on the skew-t distribution 基于 skew-t 分布的删减数据有限混合回归模型

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-10 DOI: 10.1007/s00180-024-01459-4

Jiwon Park, Dipak K. Dey, Víctor H. Lachos

{"title":"Finite mixture of regression models for censored data based on the skew-t distribution","authors":"Jiwon Park, Dipak K. Dey, Víctor H. Lachos","doi":"10.1007/s00180-024-01459-4","DOIUrl":"https://doi.org/10.1007/s00180-024-01459-4","url":null,"abstract":"Finite mixture models have been widely used to model and analyze data from heterogeneous populations. In practical scenarios, these types of data often confront upper and/or lower detection limits due to the constraints imposed by experimental apparatuses. Additional complexity arises when measures of each mixture component significantly deviate from the normal distribution, manifesting characteristics such as multimodality, asymmetry, and heavy-tailed behavior, simultaneously. This paper introduces a flexible model tailored for censored data to address these intricacies, leveraging the finite mixture of skew-t distributions. An Expectation Conditional Maximization Either (ECME) algorithm, is developed to efficiently derive parameter estimates by iteratively maximizing the observed data log-likelihood function. The algorithm has closed-form expressions at the E-step that rely on formulas for the mean and variance of truncated skew-t distributions. Moreover, a method based on general information principles is presented for approximating the asymptotic covariance matrix of the estimators. Results obtained from the analysis of both simulated and real datasets demonstrate the proposed method’s effectiveness.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A simulation model to analyze the behavior of a faculty retirement plan: a case study in Mexico 分析教师退休计划行为的模拟模型：墨西哥案例研究

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-09 DOI: 10.1007/s00180-024-01456-7

Marco Antonio Montufar-Benítez, Jaime Mora-Vargas, Carlos Arturo Soto-Campos, Gilberto Pérez-Lechuga, José Raúl Castro-Esparza

{"title":"A simulation model to analyze the behavior of a faculty retirement plan: a case study in Mexico","authors":"Marco Antonio Montufar-Benítez, Jaime Mora-Vargas, Carlos Arturo Soto-Campos, Gilberto Pérez-Lechuga, José Raúl Castro-Esparza","doi":"10.1007/s00180-024-01456-7","DOIUrl":"https://doi.org/10.1007/s00180-024-01456-7","url":null,"abstract":"The main goal in this study was to determine confidence intervals for average age, average seniority, and average money-savings, for faculty members in a university retirement system using a simulation model. The simulation—built-in Arena—considers age, seniority, and the probability of continuing in the institution as the main input random variables in the model. An annual interest rate of 7% and an average annual salary increase of 3% were considered. The scenario simulated consisted of the teacher and the university making contributions, the faculty 5% of his salary, and the university 5% of the teacher’s salary. Since the base salaries with which teachers join to university are variable, we considered a monthly salary of MXN 23 181.2, corresponding to full-time teachers with middle salaries. The results obtained by a simulation of 30 replicates showed that the confidence intervals for the average age at retirement were (55.0, 55.2) years, for the average seniority (22.1, 22.3) years, and for the average savings amount (329 795.2, 341 287.0) MXN. Moreover, the risk that a retiree of 62 years of age and more of 25 years of work, is alive after his savings runs out is approximately 98% and this happens at 64 years of age.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fitting concentric elliptical shapes under general model 一般模型下的同心椭圆形拟合

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-02-09 DOI: 10.1007/s00180-024-01460-x

{"title":"Fitting concentric elliptical shapes under general model","authors":"","doi":"10.1007/s00180-024-01460-x","DOIUrl":"https://doi.org/10.1007/s00180-024-01460-x","url":null,"abstract":"<h3>Abstract</h3> Fitting concentric ellipses is a crucial yet challenging task in image processing, pattern recognition, and astronomy. To address this complexity, researchers have introduced simplified models by imposing geometric assumptions. These assumptions enable the linearization of the model through reparameterization, allowing for the extension of various fitting methods. However, these restrictive assumptions often fail to hold in real-world scenarios, limiting their practical applicability. In this work, we propose two novel estimators that relax these assumptions: the Least Squares method (LS) and the Gradient Algebraic Fit (GRAF). Since these methods are iterative, we provide numerical implementations and strategies for obtaining reliable initial guesses. Moreover, we employ perturbation theory to conduct a first-order analysis, deriving the leading terms of their Mean Squared Errors and their theoretical lower bounds. Our theoretical findings reveal that the GRAF is statistically efficient, while the LS method is not. We further validate our theoretical results and the performance of the proposed estimators through a series of numerical experiments on both real and synthetic data.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring local explanations of nonlinear models using animated linear projections 利用动画线性投影探索非线性模型的局部解释

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-31 DOI: 10.1007/s00180-023-01453-2

Nicholas Spyrison, Dianne Cook, Przemyslaw Biecek

引用次数: 0

Semiparametric regression modelling of current status competing risks data: a Bayesian approach 现状竞争风险数据的半参数回归建模：一种贝叶斯方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-31 DOI: 10.1007/s00180-024-01455-8

Pavithra Hariharan, P. G. Sankaran

{"title":"Semiparametric regression modelling of current status competing risks data: a Bayesian approach","authors":"Pavithra Hariharan, P. G. Sankaran","doi":"10.1007/s00180-024-01455-8","DOIUrl":"https://doi.org/10.1007/s00180-024-01455-8","url":null,"abstract":"The current status censoring takes place in survival analysis when the exact event times are not known, but each individual is monitored once for their survival status. The current status data often arise in medical research, from situations that involve multiple causes of failure. Examining current status competing risks data, commonly encountered in epidemiological studies and clinical trials, is more advantageous with Bayesian methods compared to conventional approaches. They excel in integrating prior knowledge with the observed data and delivering accurate results even with small samples. Inspired by these advantages, the present study is pioneering in introducing a Bayesian framework for both modelling and analysis of current status competing risks data together with covariates. By means of the proportional hazards model, estimation procedures for the regression parameters and cumulative incidence functions are established assuming appropriate prior distributions. The posterior computation is performed using an adaptive Metropolis–Hastings algorithm. Methods for comparing and validating models have been devised. An assessment of the finite sample characteristics of the estimators is conducted through simulation studies. Through the application of this Bayesian approach to prostate cancer clinical trial data, its practical efficacy is demonstrated.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linear models with time-varying parameters: a comparison of different approaches 具有时变参数的线性模型：不同方法的比较

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-30 DOI: 10.1007/s00180-023-01452-3

Riccardo Jack Lucchetti, Francesco Valentini

引用次数: 0

Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models 有区别的统一化：推断组合状态空间（包括随机流行病模型）上马尔可夫链的新方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-26 DOI: 10.1007/s00180-024-01454-9

Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang

{"title":"Differentiated uniformization: a new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models","authors":"Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang","doi":"10.1007/s00180-024-01454-9","DOIUrl":"https://doi.org/10.1007/s00180-024-01454-9","url":null,"abstract":"We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix Q which depends on a parameter (theta ). Computing the probability distribution over states at time t requires the matrix exponential (exp ,left( tQright) ,), and inferring (theta ) from data requires its derivative (partial exp ,left( tQright) ,/partial theta ). Both are challenging to compute when the state space and hence the size of Q is huge. This can happen when the state space consists of all combinations of the values of several interacting discrete variables. Often it is even impossible to store Q. However, when Q can be written as a sum of tensor products, computing (exp ,left( tQright) ,) becomes feasible by the uniformization method, which does not require explicit storage of Q. Here we provide an analogous algorithm for computing (partial exp ,left( tQright) ,/partial theta ), the differentiated uniformization method. We demonstrate our algorithm for the stochastic SIR model of epidemic spread, for which we show that Q can be written as a sum of tensor products. We estimate monthly infection and recovery rates during the first wave of the COVID-19 pandemic in Austria and quantify their uncertainty in a full Bayesian analysis. Implementation and data are available at https://github.com/spang-lab/TenSIR.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new approach to nonparametric estimation of multivariate spectral density function using basis expansion 利用基扩展对多元谱密度函数进行非参数估计的新方法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-20 DOI: 10.1007/s00180-023-01451-4

Shirin Nezampour, Alireza Nematollahi, Robert T. Krafty, Mehdi Maadooliat

引用次数: 0

Censored broken adaptive ridge regression in high-dimension 高维度矢量破碎自适应脊回归

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-01-17 DOI: 10.1007/s00180-023-01446-1

Jeongjin Lee, Taehwa Choi, Sangbum Choi

{"title":"Censored broken adaptive ridge regression in high-dimension","authors":"Jeongjin Lee, Taehwa Choi, Sangbum Choi","doi":"10.1007/s00180-023-01446-1","DOIUrl":"https://doi.org/10.1007/s00180-023-01446-1","url":null,"abstract":"Broken adaptive ridge (BAR) is a penalized regression method that performs variable selection via a computationally scalable surrogate to (L_0) regularization. The BAR regression has many appealing features; it converges to selection with (L_0) penalties as a result of reweighting (L_2) penalties, and satisfies the oracle property with grouping effect for highly correlated covariates. In this paper, we investigate the BAR procedure for variable selection in a semiparametric accelerated failure time model with complex high-dimensional censored data. Coupled with Buckley-James-type responses, BAR-based variable selection procedures can be performed when event times are censored in complex ways, such as right-censored, left-censored, or double-censored. Our approach utilizes a two-stage cyclic coordinate descent algorithm to minimize the objective function by iteratively estimating the pseudo survival response and regression coefficients along the direction of coordinates. Under some weak regularity conditions, we establish both the oracle property and the grouping effect of the proposed BAR estimator. Numerical studies are conducted to investigate the finite-sample performance of the proposed algorithm and an application to real data is provided as a data example.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0