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  Subjects -> STATISTICS (Total: 130 journals)
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Journal of Statistical Software
Journal Prestige (SJR): 13.802
Citation Impact (citeScore): 16
Number of Followers: 16  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1548-7660 - ISSN (Online) 1548-7660
Published by American Statistical Association Homepage  [5 journals]
  • cglasso: An R Package for Conditional Graphical Lasso Inference with
           Censored and Missing Values

    • Authors: Luigi Augugliaro; Gianluca Sottile, Ernst C. Wit, Veronica Vinciotti
      Abstract: Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1 -penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data.
      PubDate: Wed, 18 Jan 2023 00:00:00 +000
       
  • deepregression: A Flexible Neural Network Framework for Semi-Structured
           Deep Distributional Regression

    • Authors: David Rügamer; Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller
      Abstract: In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package's modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.
      PubDate: Wed, 18 Jan 2023 00:00:00 +000
       
  • spsurvey: Spatial Sampling Design and Analysis in R

    • Authors: Michael Dumelle; Tom Kincaid, Anthony R. Olsen, Marc Weber
      Abstract: spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts() function flexibly accommodates several sampling design features, including stratification, varying inclusion probabilities, legacy (or historical) sites, minimum distances between sites, and two options for replacement sites. spsurvey also provides a suite of data analysis options, including categorical variable analysis (cat_analysis()), continuous variable analysis (cont_analysis()), relative risk analysis (relrisk_analysis()), attributable risk analysis (attrisk_analysis()), difference in risk analysis (diffrisk_analysis()), change analysis (change_analysis()), and trend analysis (trend_analysis()). In this manuscript, we first provide background for the GRTS algorithm and the analysis approaches and then show how to implement them in spsurvey. We find that the spatially balanced GRTS algorithm yields more precise parameter estimates than simple random sampling, which ignores spatial information.
      PubDate: Wed, 18 Jan 2023 00:00:00 +000
       
  • jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion
           Processes in Observational or Experimental Data Sets

    • Authors: Leonardo Rydin Gorjão; Dirk Witthaut, Pedro G. Lind
      Abstract: We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving secondorder corrections of any Kramers-Moyal coefficient.
      PubDate: Wed, 18 Jan 2023 00:00:00 +000
       
 
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