Journal of Statistical Distributions and Applications最新文献

A generalization to the log-inverse Weibull distribution and its applications in cancer research 对数逆威布尔分布的推广及其在癌症研究中的应用

Journal of Statistical Distributions and Applications Pub Date : 2021-12-12 DOI: 10.1186/s40488-021-00116-1

Kumar, C. Satheesh, Nair, Subha R.

引用次数: 0

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models 勒贝格空间条件概率密度函数的混合专家模型逼近

Journal of Statistical Distributions and Applications Pub Date : 2021-08-06 DOI: 10.1186/s40488-021-00125-0

H. Nguyen, TrungTin Nguyen, Faicel Chamroukhi, Geoffrey John McLachlan

引用次数: 15

Structural properties of generalised Planck distributions 广义普朗克分布的结构性质

Journal of Statistical Distributions and Applications Pub Date : 2021-08-01 DOI: 10.1186/s40488-021-00124-1

Pakes, Anthony G.

引用次数: 2

New class of Lindley distributions: properties and applications 新一类Lindley分布:属性和应用

Journal of Statistical Distributions and Applications Pub Date : 2021-07-19 DOI: 10.1186/s40488-021-00127-y

D. Hamed, Ahmad Alzaghal

引用次数: 4

Tolerance intervals in statistical software and robustness under model misspecification 统计软件的容差区间与模型错配下的鲁棒性

Journal of Statistical Distributions and Applications Pub Date : 2021-07-18 DOI: 10.1186/s40488-021-00123-2

Kyung Serk Cho, Hon Keung Tony Ng

{"title":"Tolerance intervals in statistical software and robustness under model misspecification","authors":"Kyung Serk Cho, Hon Keung Tony Ng","doi":"10.1186/s40488-021-00123-2","DOIUrl":"https://doi.org/10.1186/s40488-021-00123-2","url":null,"abstract":"A tolerance interval is a statistical interval that covers at least 100ρ% of the population of interest with a 100(1−α)% confidence, where ρ and α are pre-specified values in (0, 1). In many scientific fields, such as pharmaceutical sciences, manufacturing processes, clinical sciences, and environmental sciences, tolerance intervals are used for statistical inference and quality control. Despite the usefulness of tolerance intervals, the procedures to compute tolerance intervals are not commonly implemented in statistical software packages. This paper aims to provide a comparative study of the computational procedures for tolerance intervals in some commonly used statistical software packages including JMP, Minitab, NCSS, Python, R, and SAS. On the other hand, we also investigate the effect of misspecifying the underlying probability model on the performance of tolerance intervals. We study the performance of tolerance intervals when the assumed distribution is the same as the true underlying distribution and when the assumed distribution is different from the true distribution via a Monte Carlo simulation study. We also propose a robust model selection approach to obtain tolerance intervals that are relatively insensitive to the model misspecification. We show that the proposed robust model selection approach performs well when the underlying distribution is unknown but candidate distributions are available.","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Combining assumptions and graphical network into gene expression data analysis 结合假设和图形网络进行基因表达数据分析

Journal of Statistical Distributions and Applications Pub Date : 2021-07-08 DOI: 10.1186/s40488-021-00126-z

Demba Fofana, E. O. George, Dale Bowman

{"title":"Combining assumptions and graphical network into gene expression data analysis","authors":"Demba Fofana, E. O. George, Dale Bowman","doi":"10.1186/s40488-021-00126-z","DOIUrl":"https://doi.org/10.1186/s40488-021-00126-z","url":null,"abstract":"Analyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network relations that exist among genes. Combining these different elements cannot only improve statistical power, but also provide a better framework through which gene expression can be properly analyzed. We propose a novel statistical model that combines assumptions and gene network information into the analysis. Assumptions are important since every test statistic is valid only when required assumptions hold. So, we propose hybrid p-values and show that, under the null hypothesis of primary interest, these p-values are uniformly distributed. These proposed hybrid p-values take assumptions into consideration. We incorporate gene network information into the analysis because neighboring genes share biological functions. This correlation factor is taken into account via similar prior probabilities for neighboring genes. With a series of simulations our approach is compared with other approaches. Area Under the ROC Curves (AUCs) are constructed to compare the different methodologies; the AUC based on our methodology is larger than others. For regression analysis, AUC from our proposed method contains AUCs of Spearman test and of Pearson test. In addition, true negative rates (TNRs) also known as specificities are higher with our approach than with the other approaches. For two group comparison analysis, for instance, with a sample size of n=10, specificity corresponding to our proposed methodology is 0.716146 and specificities for t-test and rank sum are 0.689223 and 0.69797, respectively. Our method that combines assumptions and network information into the analysis is shown to be more powerful. These proposed procedures are introduced as a general class of methods that can incorporate procedure-selection, account for multiple-testing, and incorporate graphical network information into the analysis. We obtain very good performance in simulations, and in real data analysis.","PeriodicalId":52216,"journal":{"name":"Journal of Statistical Distributions and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A general stochastic model for bivariate episodes driven by a gamma sequence 由伽马序列驱动的双变量事件的一般随机模型

Journal of Statistical Distributions and Applications Pub Date : 2021-04-12 DOI: 10.1186/s40488-021-00120-5

Charles K. Amponsah, Tomasz J. Kozubowski, Anna K. Panorska

引用次数: 1

A flexible multivariate model for high-dimensional correlated count data 高维相关计数数据的灵活多元模型

Journal of Statistical Distributions and Applications Pub Date : 2021-03-16 DOI: 10.1186/s40488-021-00119-y

Alexander D. Knudson, Tomasz J. Kozubowski, Anna K. Panorska, A. Grant Schissler

引用次数: 0

Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models 零膨胀泊松和泊松障碍模型均值的广义基准推断

Journal of Statistical Distributions and Applications Pub Date : 2021-03-06 DOI: 10.1186/s40488-021-00117-0

Yixuan Zou, Jan Hannig, D. S. Young

引用次数: 1

Multivariate distributions of correlated binary variables generated by pair-copulas 由成对耦合产生的相关二元变量的多元分布

Journal of Statistical Distributions and Applications Pub Date : 2021-03-05 DOI: 10.1186/s40488-021-00118-z

Huihui Lin, N. Rao Chaganty

引用次数: 1