@article { ISI:000358354400004,
	title = {ENSEMBLING CLASSIFICATION MODELS BASED ON PHALANXES OF VARIABLES WITH APPLICATIONS IN DRUG DISCOVERY},
	journal = {ANNALS OF APPLIED STATISTICS},
	volume = {9},
	number = {1},
	year = {2015},
	month = {MAR},
	pages = {69-93},
	publisher = {INST MATHEMATICAL STATISTICS},
	type = {Article},
	address = {3163 SOMERSET DR, CLEVELAND, OH 44122 USA},
	abstract = {Statistical detection of a rare class of objects in a two-class classification problem can pose several challenges. Because the class of interest is rare in the training data, there is relatively little information in the known class response labels for model building. At the same time the available explanatory variables are often moderately high dimensional. In the four assays of our drug-discovery application, compounds are active or not against a specific biological target, such as lung cancer tumor cells, and active compounds are rare. Several sets of chemical descriptor variables from computational chemistry are available to classify the active versus inactive class; each can have up to thousands of variables characterizing molecular structure of the compounds. The statistical challenge is to make use of the richness of the explanatory variables in the presence of scant response information. Our algorithm divides the explanatory variables into subsets adaptively and passes each subset to a base classifier. The various base classifiers are then ensembled to produce one model to rank new objects by their estimated probabilities of belonging to the rare class of interest. The essence of the algorithm is to choose the subsets such that variables in the same group work well together; we call such groups phalanxes.},
	keywords = {Clustering, Model selection, quantitative structure activity relationship, random forest, ranking, rare class},
	issn = {1932-6157},
	doi = {10.1214/14-AOAS778},
	author = {Tomal, Jabed H. and Welch, William J. and Zamar, Ruben H.}
}
@article { ISI:000309793400023,
	title = {Pair copula constructions for multivariate discrete data},
	journal = {Journal of the American Statistical Association},
	volume = {107},
	number = {499},
	year = {2012},
	month = {SEP},
	pages = {1063-1072},
	publisher = {Amer Statistical Assoc},
	type = {Article},
	abstract = {Multivariate discrete response data can be found in diverse fields, including econometrics, finance, biometrics, and psychometrics. Our contribution, through this study, is to introduce a new class of models for multivariate discrete data based on pair copula constructions (PCCs) that has two major advantages. First, by deriving the conditions under which any multivariate discrete distribution can be decomposed as a PCC, we show that discrete PCCs attain highly flexible dependence structures. Second, the computational burden of evaluating the likelihood for an m-dimensional discrete PCC only grows quadratically with in. This compares favorably to existing models for which computing the likelihood either requires the evaluation of 2(m) terms or slow numerical integration methods. We demonstrate the high quality of inference function for margins and maximum likelihood estimates, both under a simulated setting and for an application to a longitudinal discrete dataset on headache severity. This article has online supplementary material.},
	keywords = {D-vine, Inference function for margins, Longitudinal data, Model selection, Ordered probit regression},
	issn = {0162-1459},
	doi = {10.1080/01621459.2012.682850},
	author = {Panagiotelis, Anastasios and Czado, Claudia and Joe, Harry}
}
@article {perlman_validity_2003,
	title = {On the validity of the likelihood ratio and maximum likelihood methods},
	journal = {Journal of Statistical Planning and Inference},
	volume = {117},
	number = {1},
	year = {2003},
	month = {nov},
	pages = {59{\textendash}81},
	abstract = {When the null or alternative hypothesis of a statistical testing problem is a union of finitely many regions of varying dimensionality, the likelihood ratio test is statistically inappropriate. Its inappropriateness is revealed not by its performance under the Neyman{\textendash}Pearson criterion but by the fact that it yields incorrect inferences in certain regions of the sample space due to its inability to adapt to the differing dimensions in the composite hypothesis. Maximum likelihood estimators and associated model selection procedures also are inappropriate for such composite models. Tests and estimators based on the p-values associated with each of the regions that constitute the composite model are more appropriate for this geometry. Similar issues arise when the boundary of the null hypothesis is a union of finitely many regions of varying dimensionality.},
	keywords = {Intersection-union test, likelihood ratio test, Model selection, Non-nested hypotheses, Union-intersection test, Varying dimensionality},
	issn = {0378-3758},
	doi = {10.1016/S0378-3758(02)00359-2},
	url = {http://www.sciencedirect.com/science/article/pii/S0378375802003592},
	author = {Perlman, Michael D. and WU, LANG}
}
