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A note on estimation in Hilbertian linear models
Adaptive estimation consistency functional regression Hilbert spaces infinite-dimensional data.
2012/9/17
We study estimation of the operator in the linear modelY = (X) +", when XandY take values in Hilbert spacesH1 andH2, respectively. Our main objective is to obtain consistency without imposing some rat...
The use of systems of stochastic PDEs as priors for multivariate models with discrete structures
Gaussian distribution multivariate stochastic PDEs discrete structures
2012/9/17
A challenge in multivariate problems with discrete structures is the inclusion of prior information that may di er in each separate structure. A particular example of this is seismic amplitude versus ...
Statistical Analysis of Autoregressive Fractionally Integrated Moving Average Models
ARFIMA models long-memory time series Whittle esti-mation exact variance matrix impulse response functions forecasting, R package.
2012/9/17
In practice, several time series exhibit long-range dependence or per-sistence in their observations, leading to the development of a number of estimation and prediction methodologies to account for t...
PAC-Bayesian Estimation and Prediction in Sparse Additive Models
Additive models sparsity regression estimation PAC-Bayesian bounds oracle inequality MCMC stochastic search.
2012/9/17
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p nparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
Competing Process Hazard Function Models for Player Ratings in Ice Hockey
Process Hazard Function Models Player Ratings Ice Hockey
2012/9/17
Evaluating the overall ability of players in the National Hockey League (NHL) is a dicult task. Existing methods such as the famous \plus/minus" statistic have many shortcomings. Standard linear regr...
We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each
data point is generated from the selected mixtu...
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves ...
Hidden Markov Models for the Activity Profile of Terrorist Groups
hidden Markov model self-exciting, terrorism terroristgroups Colombia Peru,point process changepoint detection
2012/9/19
The main focus of this work is on developing models for the ac-tivity profile of a terrorist group, detecting sudden spurtsand down-falls in this profile, and in general, tracking it over a period of ...
Causal Inference on Time Series using Structural Equation Models
Causal Inference Time Series Structural Equation Models
2012/9/19
Causal inference uses observations to infer the causal structure of the data generating system.We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). Th...
Two-step estimation of high dimensional additive models
additive model group Lasso penalized least squares.
2012/9/19
This paper investigates the two-step estimation of a high dimensional additive regression model, in which the number of nonparametric additive components is potentially larger than the sample size but...
Parameter and Structure Learning in Nested Markov Models
Parameter Structure Learning in Nested Markov Models
2012/9/19
The constraints arising from DAG mod-els with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed (!) and bidirected ($) arrow...
Models of Disease Spectra
Models Disease Spectra
2012/9/19
Case vs control comparisons have been the classical approach to the study of neurological diseases. However, most patients will not fall cleanly into either group.Instead, clinicians will typically n...
Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Iterative Conditional Fitting Gaussian Ancestral Graph Models
2012/9/19
Ancestral graph models, introduced by Richard-son and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property that is closed under...
Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
About the posterior distribution in hidden Markov Models with unknown number of states
Hidden Markov models number of components order selection Bayesian statistics posterior distribution
2012/9/19
In this paper, we investigate the asymptotic behaviour of the posterior distribution in hidden Markov models (HMMs) when using Bayesian methodology. We obtain a general asymptotic result, and give con...