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Martin Mächler
ETH Zurich
38Publications
15H-index
20.9kCitations
Publications 38
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#1Marius HofertH-Index: 15
#2Ivan KojadinovicH-Index: 21
Last.Martin MächlerH-Index: 15
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#1Marius Hofert (UW: University of Waterloo)H-Index: 15
#2Ivan KojadinovicH-Index: 21
Last.Jun Yan (UConn: University of Connecticut)H-Index: 22
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This chapter presents graphical diagnostics and statistical tests, and discusses model selection for copulas.
#1Marius Hofert (UW: University of Waterloo)H-Index: 15
#2Ivan KojadinovicH-Index: 21
Last.Jun Yan (UConn: University of Connecticut)H-Index: 22
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This chapter is concerned with more advanced topics in copula modeling such as the handling of ties, time series, and covariates (in a regression-like setting).
#1Marius Hofert (UW: University of Waterloo)H-Index: 15
#2Ivan KojadinovicH-Index: 21
Last.Jun Yan (UConn: University of Connecticut)H-Index: 22
view all 4 authors...
This chapter introduces the main copula classes and the corresponding sampling procedures, along with some copula transformations that are important for practical purposes.
#1Mollie E. Brooks (DTU: Technical University of Denmark)H-Index: 7
#2Kasper Kristensen (DTU: Technical University of Denmark)H-Index: 13
Last.Benjamin M. Bolker (McMaster University)H-Index: 47
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Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero for hosts with effective immune defenses but vary according to a negative binomial distribution for non-r...
#1Mollie E. Brooks (DTU: Technical University of Denmark)H-Index: 7
#2Kasper Kristensen (DTU: Technical University of Denmark)H-Index: 13
Last.Benjamin M. Bolker (McMaster University)H-Index: 47
view all 9 authors...
Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models. The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we...
It is shown how to set up, conduct, and analyze large simulation studies with the new R package simsalapar (= simulations simplified and launched parallel). A simulation study typically starts with determining a collection of input variables and their values on which the study depends. Computations are desired for all combinations of these variables. If conducting these computations sequentially is too time-consuming, parallel computing can be applied over all combinations of select variables. T...
#1Martin Mächler (ETH Zurich)H-Index: 15
This is a (currently very incomplete) write-up of the many smaller and larger design decisions we have made in organizing functionalities in the Matrix package. Classes: There’s a rich hierarchy of matrix classes, which you can visualize as a set of trees whose inner (and “upper”) nodes are virtual classes and only the leaves are non-virtual “actual” classes. Functions and Methods: setAs() others 1 The Matrix class structures Take Martin’s DSC 2007 talk to depict class hierarchy. — — — 1.1 Diago...
#1Martin MächlerH-Index: 15
#2Douglas M. BatesH-Index: 30
Linear algebra is at the core of many areas of statistical computing and from its inception the S language has supported numerical linear algebra via a matrix data type and several functions and operators, such as %*%, qr, chol, and solve. However, these data types and functions do not provide direct access to all of the facilities for efficient manipulation of dense matrices, as provided by the Lapack subroutines, and they do not provide for manipulation of sparse matrices. The Matrix package p...
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evalua...
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