Statistical Methodology最新文献

{"title":"Virulence Factors, Capsular Serotypes and Antimicrobial Resistance of Hypervirulent <i>Klebsiella pneumoniae</i> and Classical <i>Klebsiella pneumoniae</i> in Southeast Iran.","authors":"Sanaz Rastegar, Mohammad Moradi, Davood Kalantar-Neyestanaki, Dehdasht Ali Golabi, Hossein Hosseini-Nave","doi":"10.3947/ic.2019.0027","DOIUrl":"10.3947/ic.2019.0027","url":null,"abstract":"<p><strong>Background: </strong>The present study was conducted to investigate the distribution of virulence factors, capsular serotypes and antibiotic resistance properties of classical <i>Klebsiella pneumoniae</i> (cKP) and hypermucoviscous/hypervirulent <i>Klebsiella pneumoniae</i> (hvKP) isolated from different clinical specimens in Kerman, south-east of Iran.</p><p><strong>Materials and methods: </strong>A total of 146 <i>K. pneumoniae</i> isolates were obtained from different clinical specimens. HvKP isolates were identified using the string test. Genes of capsular serotypes K1, K2, K5, K20, K54 and K57 and virulence-associated genes, <i>rmpA</i>, <i>kfu</i>, <i>fimH</i>, <i>mrkD</i>, <i>allS</i>, <i>iutA</i>, <i>magA</i>, <i>entB</i> and <i>ybtS</i> were evaluated by PCR. Antimicrobial susceptibility was also determined using the disc diffusion method.</p><p><strong>Results: </strong>Out of 146 <i>K. pneumoniae</i> isolates, 22 (15.1 %) were hvKP. More than half of the hvKP isolates, 13 (59.1%), belonged to non-K1, K2, K5, K20, K54, K57 serotypes. Out of 22 hvKP isolates, 3 and 3 had K1 and K2 serotypes respectively. Among all isolates, <i>entB</i> 140 (95.9%) and <i>mrkD</i> 138 (94.5%) were the most common virulence genes. <i>RmpA</i>, <i>iutA</i> and <i>kfu</i> were associated with hvKP isolates (<i>P</i>-value <0.05). However, no significant difference was found in <i>fimH</i>, <i>allS</i>, <i>mrkD</i>, <i>entB</i> and <i>ybtS</i> genes between hvKP and cKP strains. HvKP exhibited significantly lower resistance rates to all antimicrobial agents than cKP, except to trimethoprim-sulphamethoxazole and ampicillin (<i>P</i>-value <0.05).</p><p><strong>Conclusion: </strong>The frequency of hvKP was low, but overall, the prevalence of virulence-related genes was higher in hvKP than cKP. HvKP was not related to specific serotypes. Furthermore, hvKP isolates were more susceptible to antimicrobial agents compared to cKP isolates.</p>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"9 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76991709","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}

{"title":"Validity of estimated prevalence of decreased kidney function and renal replacement therapy from primary care electronic health records compared with national survey and registry data in the United Kingdom.","authors":"Masao Iwagami, Laurie A Tomlinson, Kathryn E Mansfield, Anna Casula, Fergus J Caskey, Grant Aitken, Simon D S Fraser, Paul J Roderick, Dorothea Nitsch","doi":"10.1093/ndt/gfw318","DOIUrl":"10.1093/ndt/gfw318","url":null,"abstract":"<p><strong>Background: </strong>Anonymous primary care records are an important resource for observational studies. However, their external validity is unknown in identifying the prevalence of decreased kidney function and renal replacement therapy (RRT). We thus compared the prevalence of decreased kidney function and RRT in the Clinical Practice Research Datalink (CPRD) with a nationally representative survey and national registry.</p><p><strong>Methods: </strong>Among all people ≥25 years of age registered in the CPRD for ≥1 year on 31 March 2014, we identified patients with an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, according to their most recent serum creatinine in the past 5 years using the Chronic Kidney Disease Epidemiology Collaboration equation and patients with recorded diagnoses of RRT. Denominators were the entire population in each age-sex band irrespective of creatinine measurement. The prevalence of eGFR <60 mL/min/1.73 m2 was compared with that in the Health Survey for England (HSE) 2009/2010 and the prevalence of RRT was compared with that in the UK Renal Registry (UKRR) 2014.</p><p><strong>Results: </strong>We analysed 2 761 755 people in CPRD [mean age 53 (SD 17) years, men 49%], of whom 189 581 (6.86%) had an eGFR <60 mL/min/1.73 m2 and 3293 (0.12%) were on RRT. The prevalence of eGFR <60 mL/min/1.73 m2 in CPRD was similar to that in the HSE and the prevalence of RRT was close to that in the UKRR across all age groups in men and women, although the small number of younger patients with an eGFR <60 mL/min/1.73 m2 in the HSE might have hampered precise comparison.</p><p><strong>Conclusions: </strong>UK primary care data have good external validity for the prevalence of decreased kidney function and RRT.</p>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"8 1","pages":"ii142-ii150"},"PeriodicalIF":6.1,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5410977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76992345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change detection for uncertain autoregressive dynamic models through nonparametric estimation","authors":"Nadine Hilgert ,&nbsp;Ghislain Verdier ,&nbsp;Jean-Pierre Vila","doi":"10.1016/j.stamet.2016.08.003","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.08.003","url":null,"abstract":"<div><p>A new statistical approach for on-line change detection in uncertain dynamic system is proposed. In change detection problem, the distribution of a sequence of observations can change at some unknown instant. The goal is to detect this change, for example a parameter change, as quickly as possible with a minimal risk of false detection. In this paper, the observations come from an uncertain system modeled by an autoregressive model<span> containing an unknown functional component. The popular Page’s CUSUM rule is not applicable anymore since it requires the full knowledge of the model. A new detection CUSUM-like scheme is proposed, which is based on the nonparametric estimation of the unknown component from a learning sample. Moreover, the estimation procedure can be updated on-line which ensures a better detection, especially at the beginning of the monitoring procedure. Simulation trials were performed on a model describing a water treatment process and show the interest of this new procedure with respect to the classic CUSUM rule.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 96-113"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.08.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837488","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}

{"title":"A novel power-based approach to Gaussian kernel selection in the kernel-based association test","authors":"Xiang Zhan ,&nbsp;Debashis Ghosh","doi":"10.1016/j.stamet.2016.09.003","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.09.003","url":null,"abstract":"<div><p>Kernel-based association test (KAT) is a widely used tool in genetics association analysis. The performance of such a test depends on the choice of kernel. In this paper, we study the statistical power of a KAT using a Gaussian kernel. We explicitly develop a notion of analytical power function in this family of tests. We propose a novel approach to select the kernel so as to maximize the analytical power function of the test at a given test level (an upper bound on the probability<span> of making a type I error). We assess some theoretical properties of our optimal estimator, and compare its performance with some similar existing alternatives using simulation studies. Neuroimaging data from an Alzheimer’s disease study is also used to illustrate the proposed kernel selection methodology.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 180-191"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.09.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837556","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}

{"title":"A generalized inverse trinomial distribution with application","authors":"Shin Zhu Sim ,&nbsp;Seng Huat Ong","doi":"10.1016/j.stamet.2016.10.001","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.10.001","url":null,"abstract":"<div><p><span><span>This paper considers a particular generalized inverse trinomial distribution which may be regarded as the </span>convolution<span> of binomial and negative distributions for the statistical analysis of count data. This distribution has the flexibility to cater for under-, equi- and over-dispersion in the data. Some basic and probabilistic properties and tail approximation of the distribution have been derived. Conditions for the numerical stability of the two-term probability<span> recurrence formula have also been examined to facilitate computation. For the purpose of statistical analysis, test of hypothesis for equi-dispersion by the score and </span></span></span>likelihood ratio tests<span> and simulation study of their power, parameter estimation by maximum likelihood and a probability generating function<span> based methods have been considered. The versatility of the distribution is illustrated by its application to real biological data sets which exhibit under and over dispersion. It is shown that the distribution fits better than the well-known generalized Poisson and COM-Poisson distributions.</span></span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 217-233"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.10.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837557","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}

{"title":"Non-parametric Bayesian inference for continuous density hidden Markov mixture model","authors":"Najmeh Bathaee,&nbsp;Hamid Sheikhzadeh","doi":"10.1016/j.stamet.2016.10.003","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.10.003","url":null,"abstract":"<div><p><span><span>In this paper, we present a non-parametric continuous density Hidden Markov mixture model (CDHMMix model) with unknown number of mixtures for blind segmentation or clustering of sequences. In our presented model, the emission distributions of HMMs are chosen to be Gaussian with full, diagonal, or tridiagonal covariance matrices. We apply a </span>Bayesian approach to train our presented model and drive the inference of our model using the Monte Carlo Markov Chain (MCMC) method. For the multivariate Gaussian emission a method that maintains the tridiagonal structure of the covariance is introduced. Moreover, we present a new sampling method for hidden state sequences of HMMs based on the </span>Viterbi algorithm that increases the mixing rate.</p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 256-275"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.10.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837558","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}

{"title":"Estimation and goodness-of-fit in latent trait models: A comparison among theoretical approaches","authors":"Juan Carlos Bustamante ,&nbsp;Edixon Chacón","doi":"10.1016/j.stamet.2016.05.002","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.05.002","url":null,"abstract":"<div><p>Two theoretical approaches are usually employed for the fitting of ordinal data: the underlying variables approach (UV) and the item response theory (IRT). In the UV approach, limited information methods [generalized least squares (GLS) and weighted least squares<span> (WLS)] are employed. In the IRT approach, fitting is carried out with full information methods [Proportional Odds Model (POM), and the Normal Ogive (NOR)]. The four estimation methods (GLS, WLS, POM and NOR) are compared in this article at the same time, using a simulation study and analyzing the goodness-of-fit indices obtained. The parameters used in the Monte Carlo simulation arise from the application of a political action scale whose two-factor structure is well known. The results show that the estimation method employed affects the goodness-of-fit to the model. In our case, the IRT approach shows a better fitting than UV, especially with the POM method.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 83-95"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.05.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837483","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}

{"title":"Some new results on the Rényi quantile entropy Ordering","authors":"Lei Yan ,&nbsp;Dian-tong Kang","doi":"10.1016/j.stamet.2016.04.003","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.04.003","url":null,"abstract":"<div><p>Rényi (1961) proposed the Rényi entropy. Ebrahimi and Pellerey (1995) and Ebrahimi (1996) proposed the residual entropy. Recently, Nanda et al. (2014) obtained a quantile<span><span> version of the Rényi residual entropy, the Rényi residual quantile entropy (RRQE). Based on the RRQE function, they defined a new stochastic order, the Rényi quantile entropy (RQE) order, and studied some properties of this order. In this paper, we focus on further properties of this new order. Some characterizations of the RQE order are investigated, closure and reversed closure properties are obtained, meanwhile, some illustrative examples are shown. As applications of a main result, the preservation of the RQE order in several </span>stochastic models are discussed.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 55-70"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.04.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837484","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}

{"title":"Forward selection and estimation in high dimensional single index models","authors":"Shikai Luo,&nbsp;Subhashis Ghosal","doi":"10.1016/j.stamet.2016.09.002","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.09.002","url":null,"abstract":"<div><p>We propose a new variable selection and estimation technique for high dimensional single index models with unknown monotone smooth link function. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection. In this article, we propose a new penalized forward selection technique which can reduce high dimensional optimization problems to several one dimensional optimization problems by choosing the best predictor and then iterating the selection steps until convergence. The advantage of optimizing in one dimension is that the location of optimum solution can be obtained with an intelligent search by exploiting smoothness of the criterion function. Moreover, these one dimensional optimization problems can be solved in parallel to reduce computing time nearly to the level of the one-predictor problem. Numerical comparison with the LASSO and the shrinkage sliced inverse regression shows very promising performance of our proposed method.</p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 172-179"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.09.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837555","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}

{"title":"Symmetric directional false discovery rate control","authors":"Sarah E. Holte ,&nbsp;Eva K. Lee ,&nbsp;Yajun Mei","doi":"10.1016/j.stamet.2016.08.002","DOIUrl":"https://doi.org/10.1016/j.stamet.2016.08.002","url":null,"abstract":"<div><p><span>This research is motivated from the analysis of a real gene expression data that aims to identify a subset of “interesting” or “significant” genes for further studies. When we blindly applied the standard false discovery rate (FDR) methods, our biology collaborators were suspicious or confused, as the selected list of significant genes was highly unbalanced: there were ten times more under-expressed genes than the over-expressed genes. Their concerns led us to realize that the observed two-sample </span><span><math><mi>t</mi></math></span>-statistics were highly skewed and asymmetric, and thus the standard FDR methods might be inappropriate. To tackle this case, we propose a symmetric directional FDR control method that categorizes the genes into “over-expressed” and “under-expressed” genes, pairs “over-expressed” and “under-expressed” genes, defines the <span><math><mi>p</mi></math></span><span>-values for gene pairs via column permutations, and then applies the standard FDR method to select “significant” gene pairs instead of “significant” individual genes. We compare our proposed symmetric directional FDR method with the standard FDR method by applying them to simulated data and several well-known real data sets.</span></p></div>","PeriodicalId":48877,"journal":{"name":"Statistical Methodology","volume":"33 ","pages":"Pages 71-82"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.stamet.2016.08.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837482","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}