Statistics and Computing最新文献_第10页

Improvements on scalable stochastic Bayesian inference methods for multivariate Hawkes process 改进多变量霍克斯过程的可扩展随机贝叶斯推理方法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-27 DOI: 10.1007/s11222-024-10392-x

Alex Ziyu Jiang, Abel Rodriguez

引用次数: 0

Maximum likelihood estimation of log-concave densities on tree space 树空间对数凹密度的最大似然估计

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-23 DOI: 10.1007/s11222-024-10400-0

Yuki Takazawa, Tomonari Sei

{"title":"Maximum likelihood estimation of log-concave densities on tree space","authors":"Yuki Takazawa, Tomonari Sei","doi":"10.1007/s11222-024-10400-0","DOIUrl":"https://doi.org/10.1007/s11222-024-10400-0","url":null,"abstract":"Phylogenetic trees are key data objects in biology, and the method of phylogenetic reconstruction has been highly developed. The space of phylogenetic trees is a nonpositively curved metric space. Recently, statistical methods to analyze samples of trees on this space are being developed utilizing this property. Meanwhile, in Euclidean space, the log-concave maximum likelihood method has emerged as a new nonparametric method for probability density estimation. In this paper, we derive a sufficient condition for the existence and uniqueness of the log-concave maximum likelihood estimator on tree space. We also propose an estimation algorithm for one and two dimensions. Since various factors affect the inferred trees, it is difficult to specify the distribution of a sample of trees. The class of log-concave densities is nonparametric, and yet the estimation can be conducted by the maximum likelihood method without selecting hyperparameters. We compare the estimation performance with a previously developed kernel density estimator numerically. In our examples where the true density is log-concave, we demonstrate that our estimator has a smaller integrated squared error when the sample size is large. We also conduct numerical experiments of clustering using the Expectation-Maximization algorithm and compare the results with k-means++ clustering using Fréchet mean.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Do applied statisticians prefer more randomness or less? Bootstrap or Jackknife? 应用统计学家更喜欢随机性多一些还是少一些？Bootstrap 还是 Jackknife？

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-22 DOI: 10.1007/s11222-024-10388-7

Yannis G. Yatracos

{"title":"Do applied statisticians prefer more randomness or less? Bootstrap or Jackknife?","authors":"Yannis G. Yatracos","doi":"10.1007/s11222-024-10388-7","DOIUrl":"https://doi.org/10.1007/s11222-024-10388-7","url":null,"abstract":"Bootstrap and Jackknife estimates, (T_{n,B}^*) and (T_{n,J},) respectively, of a population parameter (theta ) are both used in statistical computations; n is the sample size, B is the number of Bootstrap samples. For any (n_0) and (B_0,) Bootstrap samples do not add new information about (theta ) being observations from the original sample and when (B_0<infty ,) (T_{n_0,B_0}^*) includes also resampling variability, an additional source of uncertainty not affecting (T_{n_0, J}.) These are neglected in theoretical papers with results for the utopian (T_{n, infty }^*, ) that do not hold for (B<infty .) The consequence is that (T^*_{n_0, B_0}) is expected to have larger mean squared error (MSE) than (T_{n_0,J},) namely (T_{n_0,B_0}^*) is inadmissible. The amount of inadmissibility may be very large when populations’ parameters, e.g. the variance, are unbounded and/or with big data. A palliating remedy is increasing B, the larger the better, but the MSEs ordering remains unchanged for (B<infty .) This is confirmed theoretically when (theta ) is the mean of a population, and is observed in the estimated total MSE for linear regression coefficients. In the latter, the chance the estimated total MSE with (T_{n,B}^*) improves that with (T_{n,J}) decreases to 0 as B increases.\u0000","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"54 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Forward stability and model path selection 前向稳定性和模型路径选择

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-20 DOI: 10.1007/s11222-024-10395-8

Nicholas Kissel, Lucas Mentch

{"title":"Forward stability and model path selection","authors":"Nicholas Kissel, Lucas Mentch","doi":"10.1007/s11222-024-10395-8","DOIUrl":"https://doi.org/10.1007/s11222-024-10395-8","url":null,"abstract":"Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. This work develops the idea of forward stability and proposes a novel, computationally-efficient approach to finding collections of accurate models we refer to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"41 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

The minimum covariance determinant estimator for interval-valued data 区间值数据的最小协方差行列式估计器

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-17 DOI: 10.1007/s11222-024-10386-9

Wan Tian, Zhongfeng Qin

引用次数: 0

Clustering longitudinal ordinal data via finite mixture of matrix-variate distributions 通过矩阵变量分布的有限混合物对纵向序数数据进行聚类

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-17 DOI: 10.1007/s11222-024-10390-z

Francesco Amato, Julien Jacques, Isabelle Prim-Allaz

引用次数: 0

Enmsp: an elastic-net multi-step screening procedure for high-dimensional regression Enmsp：用于高维回归的弹性网多步筛选程序

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-16 DOI: 10.1007/s11222-024-10394-9

Yushan Xue, Jie Ren, Bin Yang

引用次数: 0

Bayesian parameter inference for partially observed stochastic volterra equations 部分观测随机伏特拉方程的贝叶斯参数推断

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-14 DOI: 10.1007/s11222-024-10389-6

Ajay Jasra, Hamza Ruzayqat, Amin Wu

{"title":"Bayesian parameter inference for partially observed stochastic volterra equations","authors":"Ajay Jasra, Hamza Ruzayqat, Amin Wu","doi":"10.1007/s11222-024-10389-6","DOIUrl":"https://doi.org/10.1007/s11222-024-10389-6","url":null,"abstract":"In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. Richard et al. in Stoch Proc Appl 141:109–138, 2021) we use Euler–Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods (Jasra et al. in SIAM J Sci Comp 40:A887–A902, 2018) can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a mean square error (MSE) of (mathcal {O}(epsilon ^2)), (epsilon >0), is (mathcal {O}(epsilon ^{-tfrac{4}{2H+1}})), where H is the Hurst parameter. If one uses a single level MCMC method then the cost is (mathcal {O}(epsilon ^{-tfrac{2(2H+3)}{2H+1}})) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real data.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"19 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data 对海量生存数据进行半参数加速失效时间模型最小二乘法拟合的子采样方法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-14 DOI: 10.1007/s11222-024-10391-y

Zehan Yang, HaiYing Wang, Jun Yan

{"title":"Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data","authors":"Zehan Yang, HaiYing Wang, Jun Yan","doi":"10.1007/s11222-024-10391-y","DOIUrl":"https://doi.org/10.1007/s11222-024-10391-y","url":null,"abstract":"Massive survival data are increasingly common in many research fields, and subsampling is a practical strategy for analyzing such data. Although optimal subsampling strategies have been developed for Cox models, little has been done for semiparametric accelerated failure time (AFT) models due to the challenges posed by non-smooth estimating functions for the regression coefficients. We develop optimal subsampling algorithms for fitting semi-parametric AFT models using the least-squares approach. By efficiently estimating the slope matrix of the non-smooth estimating functions using a resampling approach, we construct optimal subsampling probabilities for the observations. For feasible point and interval estimation of the unknown coefficients, we propose a two-step method, drawing multiple subsamples in the second stage to correct for overestimation of the variance in higher censoring scenarios. We validate the performance of our estimators through a simulation study that compares single and multiple subsampling methods and apply the methods to analyze the survival time of lymphoma patients in the Surveillance, Epidemiology, and End Results program.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"168-169 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139753762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

COMBSS: best subset selection via continuous optimization COMBSS：通过连续优化选择最佳子集

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-02-12 DOI: 10.1007/s11222-024-10387-8

{"title":"COMBSS: best subset selection via continuous optimization","authors":"","doi":"10.1007/s11222-024-10387-8","DOIUrl":"https://doi.org/10.1007/s11222-024-10387-8","url":null,"abstract":"<h3>Abstract</h3> The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy. Ideally, new methods perform best subset selection faster than existing optimal methods but with comparable accuracy, or, being more accurate than methods of comparable computational speed. Here, we propose a novel continuous optimization method that identifies a subset solution path, a small set of models of varying size, that consists of candidates for the single best subset of features, that is optimal in a specific sense in linear regression. Our method turns out to be fast, making the best subset selection possible when the number of features is well in excess of thousands. Because of the outstanding overall performance, framing the best subset selection challenge as a continuous optimization problem opens new research directions for feature extraction for a large variety of regression models. ","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"16 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139753751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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