Wiley Interdisciplinary Reviews-Computational Statistics最新文献_第9页

Tensor decomposition for dimension reduction 降维的张量分解

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-07-22 DOI: 10.1002/wics.1482

Yu-Hsiang Cheng, Tzee-Ming Huang, Su‐Yun Huang

引用次数: 3

Computation of exact probabilities associated with overlapping pattern occurrences 与重叠模式出现相关的精确概率的计算

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-07-05 DOI: 10.1002/wics.1477

D. Martin

{"title":"Computation of exact probabilities associated with overlapping pattern occurrences","authors":"D. Martin","doi":"10.1002/wics.1477","DOIUrl":"https://doi.org/10.1002/wics.1477","url":null,"abstract":"Searching for patterns in data is important because it can lead to the discovery of sequence segments that play a functional role. The complexity of pattern statistics that are used in data analysis and the need of the sampling distribution of those statistics for inference renders efficient computation methods as paramount. This article gives an overview of the main methods used to compute distributions of statistics of overlapping pattern occurrences, specifically, generating functions, correlation functions, the Goulden‐Jackson cluster method, recursive equations, and Markov chain embedding. The underlying data sequence will be assumed to be higher‐order Markovian, which includes sparse Markov models and variable length Markov chains as special cases. Also considered will be recent developments for extending the computational capabilities of the Markov chain‐based method through an algorithm for minimizing the size of the chain's state space, as well as improved data modeling capabilities through sparse Markov models. An application to compute a distribution used as a test statistic in sequence alignment will serve to illustrate the usefulness of the methodology.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"11 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41535402","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

Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing 奇异谱分析作为时间序列分析和信号处理方法的特殊性和共性

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-07-04 DOI: 10.1002/wics.1487

N. Golyandina

{"title":"Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing","authors":"N. Golyandina","doi":"10.1002/wics.1487","DOIUrl":"https://doi.org/10.1002/wics.1487","url":null,"abstract":"Singular spectrum analysis (SSA), starting from the second half of the 20th century, has been a rapidly developing method of time series analysis. Since it can be called principal component analysis (PCA) for time series, SSA will definitely be a standard method in time series analysis and signal processing in the future. Moreover, the problems solved by SSA are considerably wider than that for PCA. In particular, the problems of frequency estimation, forecasting and missing values imputation can be solved within the framework of SSA. The idea of SSA came from different scientific communities, such as that of researchers in time series analysis (Karhunen–Loève decomposition), signal processing (low‐rank approximation and frequency estimation) and multivariate data analysis (PCA). Also, depending on the area of applications, different viewpoints on the same algorithms, choice of parameters, and methodology as a whole are considered. Thus, the aim of the paper is to describe and compare different viewpoints on SSA and its modifications and extensions to give people from different scientific communities the possibility to be aware of potentially new aspects of the method.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48796787","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: 42

Cauchy and other shrinkage priors for logistic regression in the presence of separation 柯西和其他收缩先验的逻辑回归存在分离

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-06-24 DOI: 10.1002/wics.1478

Joyee Ghosh

{"title":"Cauchy and other shrinkage priors for logistic regression in the presence of separation","authors":"Joyee Ghosh","doi":"10.1002/wics.1478","DOIUrl":"https://doi.org/10.1002/wics.1478","url":null,"abstract":"In recent years, the choice of prior distributions for Bayesian logistic regression has received considerable interest. It is widely acknowledged that noninformative, improper priors have to be used with caution because posterior propriety may not always hold. As an alternative, heavy‐tailed priors such as Cauchy prior distributions have been proposed by Gelman et al. (2008). The motivation for using Cauchy prior distributions is that they are proper, and thus, unlike noninformative priors, they are guaranteed to yield proper posterior distributions. The heavy tails of the Cauchy distribution allow the posterior distribution to adapt to the data. Thus Gelman et al. (2008) suggested the use of these prior distributions as a default weakly informative choice, in the absence of prior knowledge about the regression coefficients. While these prior distributions are guaranteed to have proper posterior distributions, Ghosh, Li, and Mitra (2018), showed that the posterior means may not exist, or can be unreasonably large, when they exist, for datasets with separation. In this paper, we provide a short review of the concept of separation, and we discuss how common prior distributions like the Cauchy and normal prior perform in data with separation. Theoretical and empirical results suggest that lighter tailed prior distributions such as normal prior distributions can be a good default choice when there is separation.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44053239","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: 1

Soft clustering 软聚类

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-06-17 DOI: 10.1002/wics.1480

M. Ferraro, P. Giordani

{"title":"Soft clustering","authors":"M. Ferraro, P. Giordani","doi":"10.1002/wics.1480","DOIUrl":"https://doi.org/10.1002/wics.1480","url":null,"abstract":"Clustering is one of the most used tools in data analysis. In the last decades, due to the increasing complexity of data, soft clustering has received a great deal of attention. There exist different approaches that can be considered as soft. The most known is the fuzzy approach that consists in assigning objects to clusters with membership degrees, depending on the dissimilarities between each object and all the prototypes, ranging in the unit interval. Closely related to the fuzzy approach, there is the possibilistic one that, differently from the previous one, relaxes some constraints on the membership degrees. In particular, the objects are assigned to clusters with degrees of typicalities, depending just on the dissimilarities between each object and the closest prototype. A further soft approach is the rough one. In this case, there are not degrees ranging between 0 and 1 but objects with intermediate features belong to the boundary region and are assigned to more than one cluster. Even if it is not universally recognized in the scientific community as an approach of soft clustering, from our point of view, the model‐based approach can also be considered as such. Model‐based clustering methods also produce a soft partition of the objects and the posterior probability of a component membership may play a role similar to the membership degree. The four approaches are critically described from a theoretical point of view and an empirical comparative analysis is carried out.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44284684","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: 16

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-06-14 DOI: 10.1002/wics.1448

引用次数: 0

Matrix completion from a computational statistics perspective 从计算统计的角度看矩阵补全

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-06-09 DOI: 10.1002/wics.1469

Eric C. Chi, Tianxi Li

引用次数: 12

Goodness‐of‐fit testing in sparse contingency tables when the number of variables is large 当变量数量较大时稀疏列联表的拟合优度检验

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-06-09 DOI: 10.1002/wics.1470

M. Reiser

{"title":"Goodness‐of‐fit testing in sparse contingency tables when the number of variables is large","authors":"M. Reiser","doi":"10.1002/wics.1470","DOIUrl":"https://doi.org/10.1002/wics.1470","url":null,"abstract":"The Pearson and likelihood ratio statistics are commonly used to test goodness of fit for models applied to data from a multinomial distribution. When data are from a table formed by the cross‐classification of a large number of variables, the common statistics may have low power and inaccurate Type I error level due to sparseness. One approach to finding a valid approximation to the achieved significance level (ASL) is to use a bootstrap distribution for the test statistic. For a composite null hypothesis with unknown parameters, the parametric bootstrap has been employed. The parametric bootstrap can be computationally demanding, but a recent development provides a method for computationally efficient calculation of the Pearson–Fisher statistic for very large sparse tables. Another approach employs orthogonal components of the Pearson–Fisher statistic obtained from lower‐order marginal distributions of a large cross‐classified table rather than the joint distribution. These statistics are used essentially for focused tests and have mostly been applied to latent variable models. They have very good performance for Type I error rate and power, even when applied to a sparse table. However, there are limitations when the number of variables becomes larger than 20. Some related statistics also employ lower‐order marginals, but they are not components of the Pearson–Fisher statistic. The performance of these approaches is compared for obtaining a valid ASL for a goodness‐of‐fit test applied to a very large multi‐way contingency table. The approaches are compared with a small simulation study.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44512580","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: 2

Statistical analysis of fMRI using wavelets: Big Data, denoising, large‐p‐small‐n matrices 使用小波的fMRI统计分析：大数据、去噪、大-p‐小-n矩阵

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-05-17 DOI: 10.1002/wics.1467

S. Efromovich

{"title":"Statistical analysis of fMRI using wavelets: Big Data, denoising, large‐p‐small‐n matrices","authors":"S. Efromovich","doi":"10.1002/wics.1467","DOIUrl":"https://doi.org/10.1002/wics.1467","url":null,"abstract":"Over the past decade functional Magnetic Resonance Imaging (fMRI) has been intensively used to study the complex functional network organization of the human brain and how it changes in time. An fMRI machine produces 3D time‐course cerebral images that contain hundreds of thousands of voxels and each voxel is scanned for hundreds of times. This potentially allows the researchers to explore functional connectivity on a voxel‐to‐voxel level, and also yields a number of serious statistical complications. First of all, the high‐dimension property of fMRI data turns it into Big Data. Furthermore, the study of functional brain network for so many voxels involves the problem of estimation of and simultaneous inference for large‐p‐small‐n cross‐covariance matrices. Furthermore, all problems should be solved in the presence of notoriously large fMRI noise which often forces statisticians to average signals over large areas instead of considering a network between individual voxels. An attractive alternative to the averaging, discussed in the paper, is a multiresolution wavelet analysis complemented by special procedures of estimating noise and estimation and simultaneous inference for cross‐covariance and cross‐correlation matrices for hundreds of thousands pairs of voxels, and it is fair to say that if wavelets have not been already known, fMRI applications would necessitates their creation. Both task and resting‐state fMRI are considered, and lessons from the wavelet analysis of ultra‐fast and conventional neuroplasticity fMRI experiments are presented. The article is self‐contained and does not require familiarity with wavelets or fMRI.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49302111","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

Recent advances in hyperspectral imaging for melanoma detection 高光谱成像检测黑色素瘤的最新进展

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2019-04-22 DOI: 10.1002/wics.1465

T. Johansen, Kajsa Møllersen, S. Ortega, H. Fabelo, Aday García, G. Callicó, F. Godtliebsen

{"title":"Recent advances in hyperspectral imaging for melanoma detection","authors":"T. Johansen, Kajsa Møllersen, S. Ortega, H. Fabelo, Aday García, G. Callicó, F. Godtliebsen","doi":"10.1002/wics.1465","DOIUrl":"https://doi.org/10.1002/wics.1465","url":null,"abstract":"Skin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer‐based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high‐quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale‐space theory, machine learning, and so on, are essential.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2019-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44328694","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: 39