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High Dimensional Stochastic Regression with Latent Factors, Endogeneity and Nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/26
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors, a linear com-bination of some ...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/25
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
Bessel Processes, Stochastic Volatility, and Timer Options
Bessel Processes Stochastic Volatility Timer Options
2016/1/25
Motivated by analytical valuation of timer options (an important innovation in realized variance based derivatives), we explore their novel mathematical connection with stochastic volatility and Besse...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing, dimension reduction instrument variables nonstationarity time series
2016/1/20
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
Bessel Processes, Stochastic Volatility, and Timer Options
Bessel Processes Stochastic Volatility Timer Options
2016/1/20
Motivated by analytical valuation of timer options (an important innovation in realized variance based derivatives), we explore their novel mathematical connection with stochastic volatility and Besse...
A Heuristic for Optimizing Stochastic Activity Networks with Applications to Statistical Digital Circuit Sizing
A Heuristic Optimizing Stochastic Activity Networks Applications Statistical Digital Circuit Sizing
2015/7/10
A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others ...
Nonlinear Q-Design for Convex Stochastic Control
Convex optimization nonlinear control Q-parameter stochastic control
2015/7/9
In this note we describe a version of the Q-design method that can be used to design nonlinear dynamic controllers for a discrete-time linear time-varying plant, with convex cost and constraint functi...
Performance Bounds and Suboptimal Policies for Linear Stochastic Control via LMIs
dynamic programming stochastic control convex optimization
2015/7/9
In a recent paper, the authors showed how to compute performance bounds for infinite horizon stochastic control problems with linear system dynamics and arbitrary constraints, objective, and noise dis...
Adaptive Importance Sampling via Stochastic Convex Programming
Adaptive Importance Sampling via Stochastic Convex Programming
2015/7/8
We show that the variance of the Monte Carlo estimator that is importance sampled from an exponential family is a convex function of the natural parameter of the distribution. With this insight, we pr...
Likelihood Ratio Gradient Estimation for Stochastic Recursions
Likelihood Ratio Gradient Estimation Stochastic Recursions
2015/7/8
In this paper, we develop mathematical machinery for verifying that a broad class of general state space Markov chains reacts smoothly to certain types of perturbations in the underlying transition st...
This paper is concerned with the use of grid search as a means of optimizaing an objective function that can be evaluated only through simulation. We study the question of how rapidly the number of re...
We investigate the question of when sampling a stochastic process X = {X(t): t≥0} at the times of an independent point process ψ leads to the same empirical distribution as the time-average limiting d...
Confidence Regions for Stochastic Approximation Algorithms
Confidence Regions Stochastic Approximation Algorithms
2015/7/8
In principle, known central limit theorems for stochastic approximation schemes permit the simulationist to provide confidence regions for both the optimum and optimizer of a stochastic optimization p...
A Stochastic Simulation Model of US Breast Cancer Mortality Trends from 1975 to 2000
Stochastic Simulation Model Breast Cancer Mortality Trends
2015/7/6
Background: We present a simulation model that predicts U.S. breast cancer mortality trends from 1975 to 2000 and quantifi es the impact of screening mammography and adjuvant therapy on these trends. ...
Simulation-Based Confidence Bounds for Two-Stage Stochastic Programs
Stochastic programming Linear programming Probability theory
2015/7/6
This paper provides a rigorous asymptotic analysis and justification of upper and lower confidence bounds proposed by Dantzig and Infanger (1995) for an iterative sampling-based decomposition algorith...