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Capacity Value of Additional Generation: Probability Theory and Sampling Uncertainty
Power system planning Power system operation Power system reliability Risk analysis Wind energy
2013/6/17
The concept of capacity value is widely used to quantify the contribution of additional generation (most notably renewables) within generation adequacy assessments. This paper surveys the existing pro...
Estimating Network Degree Distributions Under Sampling: An Inverse Problem, with Applications to Monitoring Social Media Networks
Estimating Network Degree Distributions Sampling An Inverse Problem Applications Monitoring Social Media Networks
2013/6/14
Networks are a popular tool for representing elements in a system and their interconnectedness. Many observed networks can be viewed as only samples of some true underlying network. Such is frequently...
An analysis of block sampling strategies in compressed sensing
Compressed Sensing blocks of measurements sampling continuous trajectories exact recovery,ℓ 1 minimization.
2013/6/17
Compressed sensing (CS) is a theory which guarantees the exact recovery of sparse signals from a few number of linear projections. The sampling schemes suggested by current CS theories are often of li...
Penalized importance sampling for parameter estimation in stochastic differential equations
Chronic wasting disease Euler-Maruyama scheme Maximum likelihood estimation Partially observed discrete sparse data Penalized importance sampling Stochastic di
2013/6/14
We consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between t...
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Ergodic convergence Markov Chain Monte Carlo Metropolis-within Gibbs composite sampling Multivariatet mixtures Simulated annealing Variational Approx-imation
2013/6/14
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible proposal distributio...
A General Family of Estimators for Estimating Population Mean in Systematic Sampling Using Auxiliary Information in the Presence of Missing Observations
Family of estimators Auxiliary information Mean square error Non-response Systematic sampling
2013/6/14
This paper proposes a general family of estimators for estimating the population mean in systematic sampling in the presence of non-response adapting the family of estimators proposed by Khoshnevisan ...
Central limit theorems for pre-averaging covariance estimators under endogenous sampling times
Central limit theorem Hitting times Market microstructure noise Nonsynchronous observa-tions Pre-averaging Time endogeneity
2013/6/13
We consider two continuous It\^o semimartingales observed with noise and sampled at stopping times in a nonsynchronous manner. In this article we establish a central limit theorem for the pre-averaged...
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference
Generalized Thompson Sampling Sequential Decision-Making Causal Inference
2013/5/2
Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problem...
Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations
Bandlimited Signal Reconstruction the Distribution Unknown Sampling Locations
2013/4/28
We study the reconstruction of bandlimited fields from samples taken at unknown but statistically distributed sampling locations. The setup is motivated by distributed sampling where precise knowledge...
Further Optimal Regret Bounds for Thompson Sampling
Further Optimal Regret Bounds Thompson Sampling
2012/11/23
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several s...
Thompson Sampling for Contextual Bandits with Linear Payoffs
Thompson Sampling Contextual Bandits Linear Payoffs
2012/11/23
Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several s...
Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs
covariance function finite population Hajek approximation Horvitz-Thompso estimator Kullback-Leibler divergence rejective sampling unequal probability sampling without replacement
2012/11/23
For fixed size sampling designs with high entropy it is well known that the variance of the Horvitz-Thompson estimator can be approximated by the H\'ajek formula. The interest of this asymptotic varia...
Diagnostics for Respondent-driven Sampling
diagnostics exploratory data analysis hard-to-reachpopulations HIV/AIDS link-tracingsampling non-ignorable design,respondent-driven sampling social networks survey sampling
2012/11/23
Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially groups most at-risk for HIV/AIDS. Data are collected through a peer-referral proc...
On the impossibility of constructing good population mean estimators in a realistic Respondent Driven Sampling model
impossibility constructing good population mean estimators realistic Respondent Driven Sampling model
2012/11/22
Current methods for population mean estimation from data collected by Respondent Driven Sampling (RDS) are based on the Horvitz-Thompson estimator together with a set of assumptions on the sampling mo...
On Set Size Distribution Estimation and the Characterization of Large Networks via Sampling
On Set Size Distribution Estimation Characterization large Networks via Sampling
2012/11/22
In this work we study the set size distribution estimation problem, where elements are randomly sampled from a collection of non-overlapping sets and we seek to recover the original set size distribut...