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Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting
Comparison nonhomogeneous regression models probabilistic wind speed forecasting
2013/6/14
In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regressi...
Model-based dose finding under model uncertainty using general parametric models
Model-based model uncertainty parametric models
2013/6/13
Statistical methodology for the design and analysis of clinical Phase II dose response studies, with related software implementation, are well developed for the case of a normally distributed, homosce...
The Estimation of Transport Logistic Processes Models on the Base of Intensive Computer Methods of Statistics
Transport Logistic Processes Models Intensive Computer Methods of Statistics
2013/6/13
The promotion work 'The Estimation of Transport Logistic Processes Models on the Base of Intensive Computer Methods of Statistics' has been worked out by Helen Afanasyeva to obtain the scientific degr...
MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
We consider the problem of detecting the periodic part of a function given the observations of some input/output tuples (xi,yi). As they are known for being powerful tools for dealing with such data, ...
Quantile Models with Endogeneity
identification treatment effects structural models instrumental variables
2013/4/28
In this article, we review quantile models with endogeneity. We focus on models that achieve identification through the use of instrumental variables and discuss conditions under which partial and poi...
Scalable Text and Link Analysis with Mixed-Topic Link Models
Document classification Community detection Topic mod-eling Link prediction Stochastic block model
2013/5/2
Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content ...
Measuring the likelihood of models for network evolution
Measuring the likelihood models network evolution
2013/4/28
Many researchers have hypothesised models which explain the evolution of the topology of a target network. The framework described in this paper gives the likelihood that the target network arose from...
Zero Intelligence Models of the Continuous Double Auction: Empirical Evidence and Generalization
Continuous double auction zero intelligence models econometric estimation consistency asymptotic normality
2013/4/28
The paper is devoted to statistical analysis of zero intelligence models of continuous double auction, in particular to an estimation of parameters of limit order books. We review "classical" zero int...
Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates
American Community Survey Bayesian hierarchical modeling GoogleTrends Spatial statistics Stochastic search variable selection
2013/4/28
The Fay-Herriot (FH) model is widely used in small area estimation and uses auxiliary information to reduce estimation variance at undersampled locations. We extend the type of covariate information u...
Node-Based Learning of Multiple Gaussian Graphical Models
graphical models structured sparsity alternating direction method of multipliers gene regulatory networks lasso multivariate normal
2013/4/28
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of ...
Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery
classification ranking ensemble random forest cluster pha-lanx
2013/4/28
We have proposed an ensemble method which aggregates over clusters of predictor variables. We form the clusters (we call phalanxes) by joining variables together. The variables in a phalanx are good t...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
Additive inverse regression models with convolution-type operators
Inverse regression Additive models Convolution-type operators Mathematical subject codes: primary 62G08 secondary 62G15 62G20
2013/4/27
In a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric m...