references.bib

@article{mayr_community_2018,
  title = {The {{Community Foehn Classification Experiment}}},
  volume = {99},
  issn = {0003-0007},
  doi = {10.1175/BAMS-D-17-0200.1},
  abstract = {Strong winds crossing elevated terrain and descending to its lee occur over mountainous areas worldwide. Winds fulfilling these two criteria are called foehn in this paper although different names exist depending on the region, the sign of the temperature change at onset, and the depth of the overflowing layer. These winds affect the local weather and climate and impact society. Classification is difficult because other wind systems might be superimposed on them or share some characteristics. Additionally, no unanimously agreed-upon name, definition, nor indications for such winds exist. The most trusted classifications have been performed by human experts. A classification experiment for different foehn locations in the Alps and different classifier groups addressed hitherto unanswered questions about the uncertainty of these classifications, their reproducibility, and dependence on the level of expertise. One group consisted of mountain meteorology experts, the other two of master's degree students who had taken mountain meteorology courses, and a further two of objective algorithms. Sixty periods of 48 h were classified for foehn\textendash{}no foehn conditions at five Alpine foehn locations. The intra-human-classifier detection varies by about 10 percentage points (interquartile range). Experts and students are nearly indistinguishable. The algorithms are in the range of human classifications. One difficult case appeared twice in order to examine the reproducibility of classified foehn duration, which turned out to be 50\% or less. The classification dataset can now serve as a test bed for automatic classification algorithms, which\textemdash{}if successful\textemdash{}eliminate the drawbacks of manual classifications: lack of scalability and reproducibility.},
  number = {11},
  journal = {Bulletin of the American Meteorological Society},
  author = {j. Mayr, Georg and Plavcan, David and Armi, Laurence and Elvidge, Andrew and Grisogono, Branko and Horvath, Kristian and Jackson, Peter and Neururer, Alfred and Seibert, Petra and Steenburgh, James W. and Stiperski, Ivana and Sturman, Andrew and Ve{\v c}enaj, {\v Z}eljko and Vergeiner, Johannes and Vosper, Simon and Z\"angl, G\"unther},
  month = aug,
  year = {2018},
  pages = {2229-2235},
  file = {/home/matthias/Zotero/storage/FLRSFI9R/Mayr et al. - 2018 - The Community Foehn Classification Experiment.pdf;/home/matthias/Zotero/storage/36JSPS3D/BAMS-D-17-0200.html}
}

@article{plavcan_automatic_2013,
  title = {Automatic and {{Probabilistic Foehn Diagnosis}} with a {{Statistical Mixture Model}}},
  volume = {53},
  issn = {1558-8424},
  doi = {10.1175/JAMC-D-13-0267.1},
  abstract = {Diagnosing foehn winds from weather station data downwind of topographic obstacles requires distinguishing them from other downslope winds, particularly nocturnal ones driven by radiative cooling. An automatic classification scheme to obtain reproducible results that include information about the (un)certainty of the diagnosis is presented. A statistical mixture model separates foehn and no-foehn winds in a measured time series of wind. In addition to wind speed and direction, it accommodates other physically meaningful classifiers such as the (potential) temperature difference to an upwind station (e.g., near the crest) or relative humidity. The algorithm was tested for Wipp Valley in the central Alps against human expert classification and a previous objective method (Drechsel and Mayr 2008), which the new method outperforms. Climatologically, using only wind information gives nearly identical foehn frequencies as when using additional covariables. A data record length of at least one year is required for satisfactory results. The suitability of mixture models for objective classification of foehn at other locations will have to be tested in further studies.},
  number = {3},
  journal = {Journal of Applied Meteorology and Climatology},
  author = {Plavcan, David and Mayr, Georg J. and Zeileis, Achim},
  month = nov,
  year = {2013},
  pages = {652-659},
  file = {/home/matthias/Zotero/storage/27E8Z7VL/Plavcan et al. - 2013 - Automatic and Probabilistic Foehn Diagnosis with a.pdf;/home/matthias/Zotero/storage/ELKBAAE3/JAMC-D-13-0267.html}
}

@article{fraley_modelbased_2002,
  title = {Model-{{Based Clustering}}, {{Discriminant Analysis}}, and {{Density Estimation}}},
  volume = {97},
  issn = {0162-1459},
  doi = {10.1198/016214502760047131},
  abstract = {Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent developments in model-based clustering for non-Gaussian data, high-dimensional datasets, large datasets, and Bayesian estimation.},
  number = {458},
  journal = {Journal of the American Statistical Association},
  author = {Fraley, Chris and Raftery, Adrian E.},
  month = jun,
  year = {2002},
  keywords = {Bayes factor,Breast cancer diagnosis,Cluster analysis,EM algorithm,Gene expression microarray data,Markov chain Monte Carlo,Mixture model,Outliers,Spatial point process},
  pages = {611-631},
  file = {/home/matthias/Zotero/storage/KG9XEFSW/Fraley und Raftery - 2002 - Model-Based Clustering, Discriminant Analysis, and.pdf;/home/matthias/Zotero/storage/UT3YMR5F/016214502760047131.html}
}

@article{leisch_flexmix_2004,
  title = {{{FlexMix}}: {{A General Framework}} for {{Finite Mixture Models}} and {{Latent Class Regression}} in {{R}}},
  volume = {11},
  copyright = {Copyright (c) 2004 Friedrich Leisch},
  issn = {1548-7660},
  shorttitle = {{{FlexMix}}},
  doi = {10.18637/jss.v011.i08},
  language = {en},
  number = {1},
  journal = {Journal of Statistical Software},
  author = {Leisch, Friedrich},
  month = oct,
  year = {2004},
  pages = {1-18},
  file = {/home/matthias/Zotero/storage/6FJLXXQK/Leisch - 2004 - FlexMix A General Framework for Finite Mixture Mo.pdf;/home/matthias/Zotero/storage/HRRG9KRC/v011i08.html}
}

@article{grun_fitting_2007,
  series = {Advances in {{Mixture Models}}},
  title = {Fitting Finite Mixtures of Generalized Linear Regressions in {{R}}},
  volume = {51},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2006.08.014},
  abstract = {R package flexmix provides flexible modelling of finite mixtures of regression models using the EM algorithm. Several new features of the software such as fixed and nested varying effects for mixtures of generalized linear models and multinomial regression for a priori probabilities given concomitant variables are introduced. The use of the software in addition to model selection is demonstrated on a logistic regression example.},
  number = {11},
  journal = {Computational Statistics \& Data Analysis},
  author = {Gr\"un, Bettina and Leisch, Friedrich},
  month = jul,
  year = {2007},
  keywords = {Concomitant variable,Finite mixture,Fixed effect,Generalized linear model},
  pages = {5247-5252},
  file = {/home/matthias/Zotero/storage/3D8V7FX2/Grün und Leisch - 2007 - Fitting finite mixtures of generalized linear regr.pdf;/home/matthias/Zotero/storage/VC47YPER/S0167947306002787.html}
}

@article{grun_flexmix_2008,
  title = {{{FlexMix Version}} 2: {{Finite Mixtures}} with {{Concomitant Variables}} and {{Varying}} and {{Constant Parameters}}},
  volume = {28},
  copyright = {Copyright (c) 2007 Bettina Gr\"un, Friedrich Leisch},
  issn = {1548-7660},
  shorttitle = {{{FlexMix Version}} 2},
  doi = {10.18637/jss.v028.i04},
  language = {en},
  number = {1},
  journal = {Journal of Statistical Software},
  author = {Gr\"un, Bettina and Leisch, Friedrich},
  month = oct,
  year = {2008},
  pages = {1-35},
  file = {/home/matthias/Zotero/storage/I8DATN95/Grün und Leisch - 2008 - FlexMix Version 2 Finite Mixtures with Concomitan.pdf;/home/matthias/Zotero/storage/CVSAEBEJ/v028i04.html}
}