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Robust quantile estimators for skewed populations 总被引：2，自引：0，他引：2
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A Gaussian process functional regression model is proposed for the analysis of batch data. Covariance structure and mean structure are considered simultaneously, with the covariance structure modeled by a Gaussian process regression model and the mean structure modeled by a functional regression model. The model allows the inclusion of covariates in both the covariance structure and the mean structure. It models the nonlinear relationship between a functional output variable and a set of functional and nonfunctional covariates. Several applications and simulation studies are reported and show that the method provides very good results for curve fitting and prediction. 相似文献
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In this paper, we consider the problem of testing the mean equality of several independent populations that contain log-normal and possibly zero observations. We first showed that the currently used methods in statistical practice, including the nonparametric Kruskal-Wallis test, the standard ANOVA F-test and its two modified versions, the Welch test and the Brown-Forsythe test, could have poor Type I error control. Then we propose a likelihood ratio test that is shown to have much better Type I error control than the existing methods. Finally, we analyze two real data sets that motivated our study using the proposed test. 相似文献
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In this paper, we consider an approach based on the adjusted signed log-likelihood ratio statistic for constructing a confidence interval for the mean of lognormal data with excess zeros. An extensive simulation study suggests that the proposed approach outperforms all the existing methods in terms of coverage probabilities and symmetry of upper and lower tail error probabilities. Finally, we analyzed two real-life datasets using the proposed approach. 相似文献
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Nick Bowman Dong Liu Patrick Paczkowski Jon Chen John Rossi Sean Mackay Adrian Bot Jing Zhou 《Proteomics》2020,20(13)
Highly multiplexed single‐cell functional proteomics has emerged as one of the next‐generation toolkits for a deeper understanding of functional heterogeneity in cell. Different from the conventional population‐based bulk and single‐cell RNA‐Seq assays, the microchip‐based proteomics at the single‐cell resolution enables a unique identification of highly polyfunctional cell subsets that co‐secrete many proteins from live single cells and most importantly correlate with patient response to a therapy. The 32‐plex IsoCode chip technology has defined a polyfunctional strength index (PSI) of pre‐infusion anti‐CD19 chimeric antigen receptor (CAR)‐T products, that is significantly associated with patient response to the CAR‐T cell therapy. To complement the clinical relevance of the PSI, a comprehensive visualization toolkit of 3D uniform manifold approximation and projection (UMAP) and t‐distributed stochastic neighbor embedding (t‐SNE) in a proteomic analysis pipeline is developed, providing more advanced analytical algorithms for more intuitive data visualizations. The UMAP and t‐SNE of anti‐CD19 CAR‐T products reveal distinct cytokine profiles between nonresponders and responders and demonstrate a marked upregulation of antitumor‐associated cytokine signatures in CAR‐T cells from responding patients. Using this powerful while user‐friendly analytical tool, the multi‐dimensional single‐cell data can be dissected from complex immune responses and uncover underlying mechanisms, which can promote correlative biomarker discovery, improved bioprocessing, and personalized treatment development. 相似文献
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Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and conditional independence structures between variables by multiple testing, which bypasses the exploration of the model space. Specifically, we introduce closed‐form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables. Their computation for all pairs of variables is shown to be extremely efficient, thereby allowing us to address large problems with thousands of nodes as required by modern applications. Moreover, we derive exact tail probabilities from the null distributions of the Bayes factors. These allow the use of any multiplicity correction procedure to control error rates for incorrect edge inclusion. We demonstrate the proposed approach on various simulated examples as well as on a large gene expression data set from The Cancer Genome Atlas. 相似文献
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Reliable estimates of past land cover are critical for assessing potential effects of anthropogenic land-cover changes on past earth surface-climate feedbacks and landscape complexity. Fossil pollen records from lakes and bogs have provided important information on past natural and human-induced vegetation cover. However, those records provide only point estimates of past land cover, and not the spatially continuous maps at regional and sub-continental scales needed for climate modelling.We propose a set of statistical models that create spatially continuous maps of past land cover by combining two data sets: 1) pollen-based point estimates of past land cover (from the REVEALS model) and 2) spatially continuous estimates of past land cover, obtained by combining simulated potential vegetation (from LPJ-GUESS) with an anthropogenic land-cover change scenario (KK10). The proposed models rely on statistical methodology for compositional data and use Gaussian Markov Random Fields to model spatial dependencies in the data.Land-cover reconstructions are presented for three time windows in Europe: 0.05, 0.2, and 6 ka years before present (BP). The models are evaluated through cross-validation, deviance information criteria and by comparing the reconstruction of the 0.05 ka time window to the present-day land-cover data compiled by the European Forest Institute (EFI). For 0.05 ka, the proposed models provide reconstructions that are closer to the EFI data than either the REVEALS- or LPJ-GUESS/KK10-based estimates; thus the statistical combination of the two estimates improves the reconstruction. The reconstruction by the proposed models for 0.2 ka is also good. For 6 ka, however, the large differences between the REVEALS- and LPJ-GUESS/KK10-based estimates reduce the reliability of the proposed models. Possible reasons for the increased differences between REVEALS and LPJ-GUESS/KK10 for older time periods and further improvement of the proposed models are discussed. 相似文献
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Lara Fontanella Luigi Ippoliti Pasquale Valentini 《Biometrical journal. Biometrische Zeitschrift》2019,61(4):918-933
In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. However, whereas many studies have demonstrated the changes in size that may occur during the period of pubertal growth spurt, shape changes have been less well investigated. Considering a group of subjects presenting normal occlusion, in this paper we thus describe a Bayesian functional ANOVA model that provides information about where and when the shape changes of the mandible occur during different stages of development. The model is developed by defining the notion of predictive process models for Gaussian process (GP) distributions used as priors over the random functional effects. We show that the predictive approach is computationally appealing and that it is useful to analyze multivariate functional data with unequally spaced observations that differ among subjects and times. Graphical posterior summaries show that our model is able to provide a biological interpretation of the morphometric findings and that they comprehensively describe the shape changes of the human mandible profiles. Compared with classical cephalometric analysis, this paper represents a significant methodological advance for the study of mandibular shape changes in two dimensions. 相似文献
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Vinicius F. Calsavara Agatha S. Rodrigues Ricardo Rocha Vera Tomazella Francisco Louzada 《Biometrical journal. Biometrische Zeitschrift》2019,61(4):841-859
Regression models in survival analysis are most commonly applied for right‐censored survival data. In some situations, the time to the event is not exactly observed, although it is known that the event occurred between two observed times. In practice, the moment of observation is frequently taken as the event occurrence time, and the interval‐censored mechanism is ignored. We present a cure rate defective model for interval‐censored event‐time data. The defective distribution is characterized by a density function whose integration assumes a value less than one when the parameter domain differs from the usual domain. We use the Gompertz and inverse Gaussian defective distributions to model data containing cured elements and estimate parameters using the maximum likelihood estimation procedure. We evaluate the performance of the proposed models using Monte Carlo simulation studies. Practical relevance of the models is illustrated by applying datasets on ovarian cancer recurrence and oral lesions in children after liver transplantation, both of which were derived from studies performed at A.C. Camargo Cancer Center in São Paulo, Brazil. 相似文献
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