530期

最新文章

• A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games AI翻译
  作者：Banerjee, Trambak; Mukherjee, Gourab; Dutta, Shantanu; Ghosh, Pulak
  作者单位：University of Southern California; University of Southern California; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore
  摘要：We develop a constrained extremely zero inflated joint (CEZIJ) modeling framework for simultaneously analyzing player activity, engagement, and dropouts (churns) in app-based mobile freemium games. Our proposed framework addresses the complex interdependencies between a player's decision to use a freemium product, the extent of her direct and indirect engagement with the product and her decision to permanently drop its usage. CEZIJ extends the existing class of joint models for longitudinal an...
• Confidence Intervals for Sparse Penalized Regression With Random Designs AI翻译
  作者：Yu, Guan; Yin, Liang; Lu, Shu; Liu, Yufeng
  作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：With the abundance of large data, sparse penalized regression techniques are commonly used in data analysis due to the advantage of simultaneous variable selection and estimation. A number of convex as well as nonconvex penalties have been proposed in the literature to achieve sparse estimates. Despite intense work in this area, how to perform valid inference for sparse penalized regression with a general penalty remains to be an active research problem. In this article, by making use of state...
• Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models AI翻译
  作者：Katzfuss, Matthias; Stroud, Jonathan R.; Wikle, Christopher K.
  作者单位：Texas A&M University System; Texas A&M University College Station; Georgetown University; University of Missouri System; University of Missouri Columbia
  摘要：We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is t...
• A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis AI翻译
  作者：Mejia, Amanda F.; Yue, Yu (Ryan); Bolin, David; Lindgren, Finn; Lindquist, Martin A.
  作者单位：Indiana University System; Indiana University Bloomington; City University of New York (CUNY) System; Baruch College (CUNY); Chalmers University of Technology; University of Gothenburg; University of Edinburgh; Johns Hopkins University
  摘要：Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the c...
• On Prediction Properties of Kriging: Uniform Error Bounds and Robustness AI翻译
  作者：Wang, Wenjia; Tuo, Rui; Wu, C. F. Jeff
  作者单位：Texas A&M University System; Texas A&M University College Station; University System of Georgia; Georgia Institute of Technology
  摘要：Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work, we obtain some error bounds for the simple and universal kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case ...
• Nonnegative Matrix Factorization Via Archetypal Analysis AI翻译
  作者：Javadi, Hamid; Montanari, Andrea
  作者单位：Rice University; Stanford University
  摘要：Given a collection of data points, nonnegative matrix factorization (NMF) suggests expressing them as convex combinations of a small set of archetypes with nonnegative entries. This decomposition is unique only if the true archetypes are nonnegative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizations. In this article, we study an approach to NMF that can be traced back to the work of Cutler and Breima...
• The Data Science Process: One Culture AI翻译
  作者：Yu, Bin; Barter, Rebecca
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Chan Zuckerberg Initiative (CZI)
• Bayesian Graphical Compositional Regression for Microbiome Data AI翻译
  作者：Mao, Jialiang; Chen, Yuhan; Ma, Li
  作者单位：Duke University
  摘要：An important task in microbiome studies is to test the existence of and give characterization to differences in the microbiome composition across groups of samples. Important challenges of this problem include the large within-group heterogeneities among samples and the existence of potential confounding variables that, when ignored, increase the chance of false discoveries and reduce the power for identifying true differences. We propose a probabilistic framework to overcome these issues by c...
• Long-Range Dependent Curve Time Series AI翻译
  作者：Li, Degui; Robinson, Peter M.; Shang, Han Lin
  作者单位：University of York - UK; University of London; London School Economics & Political Science; Australian National University
  摘要：We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of...
• Discussion of Prediction, Estimation, and Attribution by Bradley Efron AI翻译
  作者：Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert
  作者单位：Stanford University; Stanford University
  摘要：Professor Efron has presented us with a thought-provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old and new methodology, and the opportunity for both to improve through their synergy.