2015 Informs Annual Meeting

SC33

INFORMS Philadelphia – 2015

2 - A Statistical Learning Approach to Personalization in Revenue Management

3 - Pseudo Sufficient Dimension Reduction Wenbo Wu, University of Oregon, Lundquist College of Business, Eugene, OR, 97403, United States of America, wuwenbouga@gmail.com We propose a new concept of pseudo sufficient dimension reduction based on an underlying relationship between ridge regression and measurement error regression. With such a connection, we propose a general sufficient dimension reduction estimation procedure to obtain an estimate from a different subspace instead of the targeted population parameter space. Variable selection based on pseudo estimate works effectively for both highly correlated predictors and for the small n large p problem. 4 - Structured Multitask Feature Selection Fei Wang, Associate Professor, University of Connecticut, Identification of important features for specific tasks is an important problem in modern data analytics. In this talk, I will focus on the multitask feature selection problem, where multiple related tasks are considered simultaneously and the important features for each task is selected. I propose a structured optimization approach, where similar tasks share similar important feature set. I applied the proposed approach for risk factor identification in Comprehensive Geriatric Assessment. SC33 33-Room 410, Marriott Statistics and Optimization Methods for Pain Management Sponsor: Health Applications Sponsored Session Chair: Jay Rosenberger, Associate Professor, University of Texas at 371 Fairfield Way, Unit 4155, Storrs, CT, 06269, United States of America, fei_wang@uconn.edu 1 - Iterative Data Imputation for Adaptive Pain Management Yeqing Li, University of Texas Arlington, P.O. Box 19017, Arlington, TX, 76019, United States of America, yeqing.li@mavs.uta.edu, Junzhou Huang Pain management is a major global health problem. Many efforts have been devoted to developing data-driven decision models. However, the raw data is usually subject to various levels of missing. The missing values in data limit the quality and quantity of data and additionally limit the performance of the decision models. To address this problem, we proposed an iterative data imputation algorithm, which can accurately recover various kinds of missing values. 2 - Inverse Probability of Treatment Weighting for Adaptive Interdisciplinary Pain Management Victoria Chen, The University of Texas at Arlington, Dept. of Ind., Manuf., & Sys. Engr., Campus Box 19017, Arlington, TX, 76019, United States of America, vchen@uta.edu, Li Zeng, Aera Leboulluec, Robert Gatchel We present a process based on the inverse probability of treatment weighting method to address the endogeneity while estimating state transition and outcome models for a two-stage adaptive interdisciplinary pain management program. First, a method is developed for independent treatments then a general method is developed for correlated treatments. 3 - Two-stage Feature Selection for Efficient Modeling of Pain Management Data Rohit Rawat, University of Texas Arlington, P.O. Box 19017, Arlington, TX, 76019, United States of America, rohit.rawat@mavs.uta.edu, Michael Manry We use data from a pain management study in which treatment variables and two pain intensity metrics were recorded in two treatment stages. Data sets for the 235 subjects had 899 features for stage one and 1889 for stage two. A two- stage feature selection algorithm was developed that fits a piecewise linear network to the data, and removes useless inputs. We prevent overfitting through the use of random probes and regularization. The method results in smaller datasets and reduced testing error. Arlington, Box 19017 UT, Arlington, TX, 76019, United States of America, jrosenbe@uta.edu

Zachary Owen, Massachusetts Institute of Technology, 77 Massachusetts Ave, E40-149, Cambridge, MA, 02139, United States of America, zowen@mit.edu, David Simchi-levi, Xi Chen, Clark Pixton We develop a framework for modeling personalized decision problems which gives a data driven algorithm for revenue maximization using contextual information. We apply our method to pricing and assortment optimization. We prove a high-probability bound on the gap between the revenue of the estimated policy and the revenue generated under full knowledge of the demand distribution. We demonstrate the performance of our method on both airline seating data and simulated data. 3 - Uncovering Hidden Decision Processes through Integration of Independent Databases Nooshin Valibeig, Northeastern University, 334 Snell Engineering, Boston, MA, 02115, United States of America, nooshin.valibeig@gmail.com, Jacqueline Griffin Data is a key driver in analyzing and evaluating the effectiveness of decision processes. We develop an algorithm to uncover hidden information about decision processes in resource allocation systems. Specifically, the algorithm joins event-oriented and snapshot-in-time databases to extract new knowledge about decision processes. The precision and robustness of the algorithm is quantified with simulated data. A case study with hospital patient flow data is presented. 4 - Reinforcement Learning Algorithms for Regret Minimization in Structured Markov Decision Processes Theja Tulabandhula, Xerox Research Centre India, Bangalore, Bangalore, India, theja2t@gmail.com, Prabuchandran K. J., Tejas Bodas For several RL problems the optimal policy of the underlying Markov Decision Process (MDP) is characterized by a known structure. We develop new RL algorithms that exploit the structure of the optimal policy to minimize regret. Numerical experiments on MDPs with structured optimal policies show that our algorithms have better performance than current state of the art, are easy to implement, have a smaller run-time, can be parallelized and require less number of random number generations. SC32 32-Room 409, Marriott Special Topics in Supervised Learning: Variable Selections and Dimension Reductions Chair: Chaojiang Wu, Drexel University, 727 Gerri LeBow Hall, 3220 Market Street, Philadelphia, PA, 19104, United States of America, cw578@drexel.edu 1 - Sparse Nonlinear Feature Selection by Locally Discriminative Constraints Chuanren Liu, Assistant Professor, Drexel University, 3220 Market St, Philadelphia, PA, 19104, United States of America, liuchuanren@gmail.com, Kai Zhang We present an approach to sparse nonlinear feature selection for K-nearest neighbor (KNN) classification. First, the factors for selecting feature are optimized with locally discriminative constraints, which encourage smaller distances between neighbors from the same class and larger distances between neighbors from different classes. Then, we use lasso to achieve the sparse feature selection. We also show an interesting connection between our formulation and the support vector machines (SVMs). 2 - Maximum Tangent Likelihood Estimation and Robust Variable Selection Yichen Qin, Assistant Professor, University of Cincinnati, 2925 Campus Green Dr., Cincinnati, OH, 45221, United States of America, yichenqin@gmail.com, Yan Yu, Yang Li, Shaobo Li In this article, we propose a new class of likelihood function, called Tangent Likelihood function, that can be used to obtain robust estimates, termed as Maximum Tangent Likelihood Estimator (MtLE). We show that the MtLE is root- n consistent and asymptotically normally distributed. Furthermore, we consider robust variable selection based on our proposed tangent likelihood function. The proposed MtLE-Lasso can perform robust estimation and variable selection simultaneously and consistently. Sponsor: Data Mining Sponsored Session

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