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University of Economics and Business",[108,129],"{\"ror\":\"03yn8s215\",\"openalex\":\"I102248843\"}","2026-05-14T01:57:42.255396807","2026-06-01T06:05:49.837894431","companyType","UNIVERSITY","foundedDate","headquarters","Vienna, Austria","country","AT","employeeCount","fundingStage","totalFunding","valuation","website","https://www.wu.ac.at","stockSymbol","uscc","crunchbaseUrl","Wirtschaftsuniversität Wien","company_I97018004","Stanford University",[137,131,138],"{\"ror\":\"00f54p054\",\"openalex\":\"I97018004\",\"wikidata\":\"Q41506\"}","2026-05-14T00:28:55.358107961","2026-07-02T09:08:01.129598041","US","Leland Stanford Junior University","Universidad Stanford","company_I1850255","University of Basel",[140,146,147,148],"{\"ror\":\"02s6k3f65\",\"openalex\":\"I1850255\",\"wikidata\":\"https://www.wikidata.org/wiki/Q372608\"}","2026-05-14T00:33:23.296572019","2026-06-01T04:42:38.817625872","CH","Università di Basilea","Universität Basel","Université de Bâle","company_I8204097","Ludwig-Maximilians-Universität München",[156,157,150,158,159,160],"{\"ror\":\"05591te55\",\"openalex\":\"I8204097\"}","2026-05-14T00:32:14.252345873","2026-06-01T04:20:42.307341416","DE","LMU München","Ludwig Maximilian University of Munich","Ludwig-Maximilians-Universität in Munich","University of Munich","Universität München",{"_15":306,"_29":307,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":308,"_39":-5,"_40":41,"_42":309,"_44":310,"_176":311,"_178":-5,"_179":312,"_48":307,"_181":313,"_183":314,"_185":186,"_187":315,"_52":316,"_190":-5,"_191":317,"_193":318,"_195":227,"_58":307,"_59":-5},{"_15":294,"_29":295,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":296,"_39":-5,"_40":41,"_42":297,"_44":298,"_176":299,"_178":-5,"_179":300,"_48":295,"_181":301,"_183":302,"_185":207,"_187":223,"_52":303,"_190":-5,"_191":304,"_193":305,"_195":227,"_58":295,"_59":-5},{"_15":278,"_29":279,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":280,"_39":-5,"_40":41,"_42":281,"_44":282,"_176":283,"_178":-5,"_179":284,"_48":279,"_181":285,"_183":286,"_185":287,"_187":223,"_52":288,"_190":-5,"_191":289,"_193":290,"_195":227,"_58":279,"_59":-5},{"_15":266,"_29":267,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":268,"_39":-5,"_40":41,"_42":269,"_44":270,"_176":271,"_178":-5,"_179":272,"_48":267,"_181":273,"_183":274,"_185":207,"_187":188,"_52":275,"_190":-5,"_191":276,"_193":-5,"_195":86,"_58":267,"_59":-5},{"_15":256,"_29":257,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":258,"_39":-5,"_40":41,"_42":259,"_44":260,"_176":261,"_178":-5,"_179":262,"_48":257,"_181":263,"_183":221,"_185":222,"_187":223,"_52":264,"_190":-5,"_191":265,"_193":-5,"_195":227,"_58":257,"_59":-5},{"_15":242,"_29":243,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":244,"_39":-5,"_40":41,"_42":245,"_44":246,"_176":247,"_178":-5,"_179":248,"_48":243,"_181":249,"_183":250,"_185":251,"_187":223,"_52":252,"_190":-5,"_191":253,"_193":-5,"_195":227,"_58":243,"_59":-5},{"_15":229,"_29":230,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":231,"_39":-5,"_40":41,"_42":232,"_44":233,"_176":234,"_178":-5,"_179":235,"_48":230,"_181":-5,"_183":236,"_185":207,"_187":237,"_52":238,"_190":-5,"_191":239,"_193":-5,"_195":86,"_58":230,"_59":-5},{"_15":213,"_29":214,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":215,"_39":-5,"_40":41,"_42":216,"_44":217,"_176":218,"_178":-5,"_179":219,"_48":214,"_181":220,"_183":221,"_185":222,"_187":223,"_52":224,"_190":-5,"_191":225,"_193":226,"_195":227,"_58":214,"_59":-5},{"_15":199,"_29":200,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":201,"_39":-5,"_40":41,"_42":202,"_44":203,"_176":-5,"_178":-5,"_179":204,"_48":200,"_181":205,"_183":206,"_185":207,"_187":208,"_52":209,"_190":-5,"_191":210,"_193":-5,"_195":86,"_58":200,"_59":-5},{"_15":171,"_29":172,"_31":-5,"_32":-5,"_33":-5,"_34":-5,"_35":36,"_37":173,"_39":-5,"_40":41,"_42":174,"_44":175,"_176":177,"_178":-5,"_179":180,"_48":172,"_181":182,"_183":184,"_185":186,"_187":188,"_52":189,"_190":-5,"_191":192,"_193":194,"_195":86,"_58":172,"_59":-5},"paper_W2103845670","Residual-Based Shadings for Visualizing (Conditional) Independence","{\"openalex\":\"W2103845670\",\"doi\":\"10.1198/106186007x237856\"}","2026-05-22T07:07:56.268171904","2026-05-22T07:07:56.268173112","doi","10.1198/106186007x237856","arxivId","openalexId","W2103845670","abstractText","Residual-based shadings for enhancing mosaic and association plots to visualize independence models for contingency tables are extended in two directions: (a) perceptually uniform HCL colors are used and (b) the result of an associated significance test is coded by the appearance of color in the visualization. For obtaining (a), a general strategy for deriving diverging palettes in the perceptually-based HCL space is suggested. As for (b), cut offs that control the appearance of color are computed in a data-driven way based on the conditional permutation distribution of maximum-type test statistics. The shadings are first established for the case of independence in 2-way tables and then extended to more general independence models for multi-way tables, including in particular conditional independence problems.","publishedDate","2007-09-01","publishedYear",2007,"venue","Journal of Computational and Graphical Statistics",257,"authors","keywords",[196,197,198],"pdfUrl","http://statmath.wu.ac.at/~zeileis/papers/Zeileis+Meyer+Hornik-2007.pdf","topVenue","Data Visualization and Analytics","Sensory Analysis and Statistical Methods","Statistical Methods and Applications","paper_W3123727659","Regression Models for Count Data in R","{\"openalex\":\"W3123727659\"}","2026-05-20T19:38:44.305314344","2026-05-20T19:38:44.305389154","W3123727659","The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inﬂated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inﬂated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences—better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be ﬁtted, inspected and tested in practice.","2008-06-01",2008,"DOAJ (DOAJ: Directory of Open Access Journals)",318,[211,212],"Statistical Methods and Bayesian Inference","Bayesian Modeling and Causal Inference","paper_W2104060766","The Strucplot Framework: Visualizing Multi-way Contingency Tables withvcd","{\"openalex\":\"W2104060766\",\"doi\":\"10.18637/jss.v017.i03\"}","2026-05-20T02:53:02.209615807","2026-05-20T02:53:03.183369937","10.18637/jss.v017.i03","W2104060766","This paper describes the \"strucplot\" framework for the visualization of multi-way contingency tables. Strucplot displays include hierarchical conditional plots such as mosaic, association, and sieve plots, and can be combined into more complex, specialized plots for visualizing conditional independence, GLMs, and the results of independence tests. The framework's modular design allows flexible customization of the plots' graphical appearance, including shading, labeling, spacing, and legend, by means of \"graphical appearance control\" functions. The framework is provided by the R package vcd.","2006-01-01",2006,"Journal of Statistical Software",359,[196,197,228],"https://research.wu.ac.at/ws/files/19841445/document.pdf",true,"Data Analysis with R","paper_W2033371754","Open-source machine learning: R meets Weka","{\"openalex\":\"W2033371754\",\"doi\":\"10.1007/s00180-008-0119-7\"}","2026-05-19T17:00:09.198978756","2026-05-19T17:00:09.200349967","10.1007/s00180-008-0119-7","W2033371754","2008-05-13","Computational Statistics",394,[240,241,228],"Data Mining Algorithms and Applications","Machine Learning and Data Classification","paper_W2751108361","Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R","{\"openalex\":\"W2751108361\",\"doi\":\"10.18637/jss.v095.i01\"}","2026-05-15T09:28:17.206714531","2026-05-21T06:48:38.184895303","10.18637/jss.v095.i01","W2751108361","Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, and other social sciences. They are employed to adjust the inference following estimation of a standard least-squares regression or generalized linear model estimated by maximum likelihood. Although many publications just refer to","2020-01-01",2020,636,[254,228,255],"Bayesian Methods and Mixture Models","Statistical Methods and Inference","paper_W2155729027","Object-Oriented Computation of Sandwich Estimators","{\"openalex\":\"W2155729027\",\"doi\":\"10.18637/jss.v016.i09\"}","2026-05-15T04:56:11.234507308","2026-05-21T03:31:17.180551607","10.18637/jss.v016.i09","W2155729027","Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed.",699,[211,255,228],"paper_W2045803758","Model-Based Recursive Partitioning","{\"openalex\":\"W2045803758\",\"doi\":\"10.1198/106186008x319331\"}","2026-05-15T00:54:28.278424867","2026-05-21T00:29:45.835756257","10.1198/106186008x319331","W2045803758","Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a dataset; (2) test for parameter instability over a set of partitioning variables; (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability; (4) repeat the procedure in each of the daughter nodes. The algorithm yields a partitioned (or segmented) parametric model that can be effectively visualized and that subject-matter scientists are used to analyzing and interpreting.","2008-05-28",769,[254,255,277],"Financial Risk and Volatility Modeling","paper_W2157963336","kernlab- AnS4Package for Kernel Methods inR","{\"openalex\":\"W2157963336\",\"doi\":\"10.18637/jss.v011.i09\"}","2026-05-14T05:15:01.225357989","2026-05-21T07:29:32.184839987","10.18637/jss.v011.i09","W2157963336","kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.","2004-01-01",2004,1832,[291,292,293],"https://research.wu.ac.at/ws/files/17227362/kernlab.pdf","Gaussian Processes and Bayesian Inference","Neural Networks and Applications","Time Series Analysis and Forecasting","paper_W1863440143","Regression Models for Count Data inR","{\"openalex\":\"W1863440143\",\"doi\":\"10.18637/jss.v027.i08\"}","2026-05-14T03:53:41.356057852","2026-05-21T05:46:23.180433329","10.18637/jss.v027.i08","W1863440143","The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences-better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice.","2008-01-01",2339,[211,255,254],"https://research.wu.ac.at/ws/files/19799951/Zeileis_etal_2008_JSS_Regression-Models-for-Count-Data-in-R.pdf","paper_W1875061881","Bias in random forest variable importance measures: Illustrations, sources and a solution","{\"openalex\":\"W1875061881\",\"doi\":\"10.1186/1471-2105-8-25\"}","2026-05-14T02:11:12.395972466","2026-05-21T03:59:19.201774953","10.1186/1471-2105-8-25","W1875061881","BACKGROUND: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. RESULTS: Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. CONCLUSION: We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. 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Achim Zeileis 人物

Achim Zeileis · 相关文章