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Counterfactual actions in graphical models based on local independence
causal inference event history analysis marked point
2011/7/5
We consider a framework for counterfactual statistical analysis with graphical models based on marked point processes. The main idea is to treat the counterfactual scenario as just another probability...
State-Observation Sampling and the Econometrics of Learning Models
Hidden Markov model particle filter state-observation sampling learning indirect inference forecasting state space model value at risk
2011/6/20
In nonlinear state-space models, sequential learning about the hidden state can proceed
by particle filtering when the density of the observation conditional on the state is available
analytically (...
A Functional Version of the ARCH Model
ARCH financial data functional time series high-frequency data weak-dependence
2011/6/16
Improvements in data acquisition and processing techniques have lead to an almost continuous
flow of information for financial data. High resolution tick data are available and can be quite convenien...
Optimal Reinforcement Learning for Gaussian Systems
Optimal Reinforcement Learning Gaussian Systems
2011/7/5
The exploration-exploitation tradeoff is among the central challenges of reinforcement learning. A hypothetical exact Bayesian learner would provide the optimal solution, but is intractable in general...
Are adaptive allocation designs beneficial for improving power in binary response trials?
Neyman allocation adaptive design asymptotic power Normal approximation Pitman effi ciency Bahadur effi ciency large deviations
2011/3/24
We consider the classical problem of selecting the best of two treatments in clinical trials with binary response. The target is to find the design that maximizes the power of the relevant test. Many ...
A Generalized Least Squares Matrix Decomposition
matrix decomposition,singular value decomposition,transposable data,principal components analysis,sparse principal components analysis,functional prin-cipal components analysis,spatio-temporal data
2011/3/21
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
A Generalized Least Squares Matrix Decomposition
matrix decomposition singular value decomposition transposable data principal components analysis, sparse principal components analysis functional prin-cipal components analysis spatio-temporal data
2011/3/23
Variables in high-dimensional data sets common in neuroimaging, spatial statistics, time series and genomics often exhibit complex dependencies. Conventional multivariate analysis techniques often ign...
Selection models with monotone weight functions in meta analysis
global constrained optimization meta analysis monotone non-increasing selection bias
2011/3/24
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the res...
Multivariate stratified sampling by stochastic multiobjective optimisation
Multivariate stratified random sampling multiobjective E-model
2011/7/5
This work considers the allocation problem for multivariate stratified random sampling as a problem of integer non-linear stochastic multiobjective mathematical programming.
The beta-Bernoulli process provides a Bayesian nonparametric prior for models involving collections of binary-valued features.
Parameter estimation in a spatial unit root autoregressive model
Spatial autoregressive processes unit root models
2011/3/23
Spatial autoregressive model $X_{k,\ell}=\alpha X_{k-1,\ell}+\beta X_{k,\ell-1}+\gamma X_{k-1,\ell-1}+\epsilon_{k,\ell}$ is investigated in the unit root case, that is when the parameters are on the b...
Parameter estimation in a spatial unit root autoregressive model
Spatial autoregressive processes unit root models
2011/3/22
Spatial autoregressive model $X_{k,\ell}=\alpha X_{k-1,\ell}+\beta X_{k,\ell-1}+\gamma X_{k-1,\ell-1}+\epsilon_{k,\ell}$ is investigated in the unit root case, that is when the parameters are on the b...
When a matrix A with n columns is known to be well approximated by a linear combination of basis matrices B_1,..., B_p, we can apply A to a random vector and solve a linear system to recover this line...
Risk,VaR,CVaR and their associated Portfolio Optimizations when Asset Returns have a Multivariate Student T Distribution
VaR CVaR Portfolio Optimization VaR Optimization CVaR Optimization Optimisation
2011/3/25
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of ...
Neyman-Pearson classification, convexity and stochastic constraints
binary classifi cation Neyman-Pearson paradigm anomaly detection stochastic constraint convexity empirical risk minimization chance constrained optimization
2011/3/25
Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of c...