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A Nonparametric Joint Assortment and Price Choice Model

MANAGemENT SCIENCE 2017年第9期63卷 3128-3145页

作者： Jagabathula, Srikanth Rusmevichientongb, Paat NYU Stern Sch Business New York NY 10012 USA Univ Southern Calif Marshall Sch Business Los Angeles CA 90089 USA

The selection of products and prices offered by a firm significantly impacts its profits. Existing approaches do not provide flexible models that capture the joint effect of assortment and price. We propose a nonparametric framework in which each customer is represented by a particular price threshold and a particular preference list over the alternatives. The customers follow a two-stage choice process;they consider the set of products with prices less than the threshold and choose the most preferred product from the set considered. We develop a tractable nonparametric expectation maximization (em) algorithm to fit the model to the aggregate transaction data and design an efficient algorithm to determine the profit-maximizing combination of offer set and price. We also identify classes of pricing structures of increasing complexity, which determine the computational complexity of the estimation and decision problems. Our pricing structures are naturally expressed as business constraints, allowing a manager to trade off pricing flexibility with computational burden.

关键词： nonparametric choice models joint assortment and price optimization em algorithm transaction data

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Predicting Waterflooding Performance in Low-Permeability Reservoirs With Linear Dynamical Systems

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SPE JOURNAL 2017年第5期22卷 1596-1608页

作者： Wang, Dashun Niu, Di Li, Huazhou Andy Univ Alberta Joint Program Comp Engn & Petr Engn Edmonton AB Canada Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Univ Alberta Petr Engn Edmonton AB Canada

Several interwell connectivity models such as multiple linear regression (MLR) and the capacitance model (CM) have been proposed to model waterflooding performance in high-permeability reservoirs on the basis of observed production data. However, the existing methods are not effective at characterizing the behavior of transient flows that are prevalent in low-permeability reservoirs. This paper presents a novel dynamic waterflooding model that is based on linear dynamical systems (LDSs) to characterize the injection/production relationships in an oil field during both stationary and nonstationary production phases. We leverage a state-space model (SSM), in which the changing rates of control volumes between injector/producer pairs in the reservoir of interest serve as time-varying hidden states, depending on the reservoir condition. Thus, the model can better characterize the transient dynamics in low-permeability reservoirs. We propose a self-learning procedure for the model to train its parameters as well as the evolution of the hidden states only on the basis of past observations of injection and production rates. We tested the LDS method in comparison with the state-of-the-art CM method in a wide range of synthetic reservoir models including both high-permeability and low-permeability reservoirs, as well as various dynamic scenarios involving varying bottomhole pressure (BHP) of producers, injector shut-ins, and reservoirs of larger scales. We also tested LDS on the real production data collected from Changqing oil field containing low-permeability formations. Testing results demonstrate that an LDS significantly outperforms CM in terms of modeling and predicting waterflooding performance in low-permeability reservoirs and various dynamic scenarios.

关键词： flow in porous media enhanced recovery Upstream Oil & Gas injector october 2017 production rate em algorithm Injection Rate Artificial Intelligence Fluid Dynamics

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A robust modified Gaussian mixture model with rough set for image segmentation

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NEUROCOMPUTING 2017年 266卷 550-565页

作者： Ji, Zexuan Huang, Yubo Xia, Yong Zheng, Yuhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci CMCC Xian 710072 Shaanxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Accurate image segmentation is an essential step in image processing, where Gaussian mixture models with spatial constraint play an important role and have been proven effective for image segmentation. Nevertheless, most methods suffer from one or more challenges such as limited robustness to outliers, over-smoothness for segmentations, sensitive to initializations and manually setting parameters. To address these issues and further improve the accuracy for image segmentation, in this paper, a robust modified Gaussian mixture model combining with rough set theory is proposed for image segmentation. Firstly, to make the Gaussian mixture models more robust to noise, a new spatial weight factor is constructed to replace the conditional probability of an image pixel with the calculation of the probabilities of pixels in its immediate neighborhood. Secondly, to further reduce the over-smoothness for segmentations, a novel prior factor is proposed by incorporating the spatial information amongst neighborhood pixels. Finally, each Gaussian component is characterized by three automatically determined rough regions, and accordingly the posterior probability of each pixel is estimated with respect to the region it locates. We compare our algorithm to state-of-the-art segmentation approaches in both synthetic and real images to demonstrate the superior performance of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image segmentation Gaussian mixture model Markov random field Spatial information Rough set theory em algorithm

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Two-step and likelihood methods for joint models of longitudinal and survival data

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第8期46卷 6019-6033页

作者： Ye, Qian Wu, Lang Univ British Columbia Dept Stat Vancouver BC V6T 1Z2 Canada

We compare the commonly used two-step methods and joint likelihood method for joint models of longitudinal and survival data via extensive simulations. The longitudinal models include LME, GLMM, and NLME models, and the survival models include Cox models and AFT models. We find that the full likelihood method outperforms the two-step methods for various joint models, but it can be computationally challenging when the dimension of the random effects in the longitudinal model is not small. We thus propose an approximate joint likelihood method which is computationally efficient. We find that the proposed approximation method performs well in the joint model context, and it performs better for more "continuous" longitudinal data. Finally, a real AIDS data example shows that patients with higher initial viral load or lower initial CD4 are more likely to drop out earlier during an anti-HIV treatment.

关键词： Approximation method em algorithm Likelihood method Mixed effects model Two-step methods

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Estimating the causal effects in randomized trials for survival data with a cure fraction and non compliance

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第8期46卷 4065-4087页

作者： Gao, Xiang Zheng, Ming Fudan Univ Sch Management Dept Stat Shanghai Peoples R China

We consider causal inference in randomized studies for survival data with a cure fraction and all-or-none treatment non compliance. To describe the causal effects, we consider the complier average causal effect (CACE) and the complier effect on survival probability beyond time t (CESP), where CACE and CESP are defined as the difference of cure rate and non cured subjects' survival probability between treatment and control groups within the complier class. These estimands depend on the distributions of survival times in treatment and control groups. Given covariates and latent compliance type, we model these distributions with transformation promotion time cure model whose parameters are estimated by maximum likelihood. Both the infinite dimensional parameter in the model and the mixture structure of the problem create some computational difficulties which are overcome by an expectation-maximization (em) algorithm. We show the estimators are consistent and asymptotically normal. Some simulation studies are conducted to assess the finite-sample performance of the proposed approach. We also illustrate our method by analyzing a real data from the Healthy Insurance Plan of Greater New York.

关键词： All-or-none compliance Cure model em algorithm Non parametric likelihood Transformation model 62N01 62N02 62E20

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Detection of uneaten fish food pellets in underwater images for aquaculture

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AQUACULTURAL ENGINEERING 2017年第PartB期78卷 85-94页

作者： Li, Dawei Xu, Lihong Liu, Huanyu Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Huawei Technol Co Ltd Shanghai 200120 Peoples R China

The waste of fish food has always been a serious problem in aquaculture. On one hand, the leftover fish food spawns a big economic loss in the industry because feedstuff accounts for a large proportion of the investment. On the other hand, the left over fish food may pollute the water and worsen the living environment of aquatic products. In this paper, we develop an adaptive thresholding method for detecting uneaten fish food in underwater images. To deal with non -uniform illumination in underwater environments, we focus on analyzing the local histogram of intensities in a mask for each pixel. The Expectation-maximization-guided Gaussian mixture is used to fit the histogram to figure out its type, and then an adaptive threshold is computed accordingly. At last the central pixel of the mask is compared with the threshold to generate the binary detection result. Experimental results show that the proposed method obtains desirable detection of leftover fish food in many underwater environments with different water turbidity levels and with different extent of unevenness of illumination. In the four test underwater environments, the lowest True Positive Rate of the proposed method is higher than 80%, and the highest rate reaches 95.9%. The False Positive Rates of the proposed method are no higher than 2.7%.

关键词： Fish food detection Aquaculture Image segmentation Gaussian Mixture Model em algorithm Non-uniform illumination

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Estimating the Propagation of Interdependent Cascading Outages With Multi-Type Branching Processes

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IEEE TRANSACTIONS ON POWER SYSTemS 2017年第2期32卷 1212-1223页

作者： Qi, Junjian Ju, Wenyun Sun, Kai Argonne Natl Lab Div Energy Syst Argonne IL 60439 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

In this paper, the multitype branching process is applied to describe the statistics and interdependencies of line outages, the load shed, and isolated buses. The offspring mean matrix of the multitype branching process is estimated by the Expectation Maximization (em) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of outages is estimated by the multitype branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of outages. Compared with empirically estimating the joint distribution of the total outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total outages even when there are no data of them.

关键词： Cascading blackout em algorithm interdependency joint distribution Lagrange-Good inversion multi-type branching process reliability resilience

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Mixed proportional hazard models with continuous finite mixture unobserved heterogeneity: an application to Canadian firm survival

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2017年第1期33卷 81-94页

作者： Huynh, Kim Voia, Marcel Bank Canada 234 Wellington St Ottawa ON K1A 0G9 Canada Carleton Univ Dept Econ 1125 Colonel Dr Ottawa ON K1S 5B6 Canada

This paper proposes a methodology that accounts for the selection effect due to non-random entry in duration models using latent-class models. A mixed proportional hazard model with continuous finite mixture unobserved heterogeneity (MPH-CFM) is introduced to correct for the potential bias induced by the selection effect. Conditions for identification, consistency, and asymptotic normality of the MPH-CFM are provided. The estimator is used to investigate the duration of new entrant Canadian manufacturing firms. For the current application, the MPH-CFM is compared with alternative duration models and found to be superior. empirically, the results indicate that there are two classes of firms. Class I starts with high hazard and decreases non-monotonically while Class II has a negligible hazard. These empirical results can be used to understand alternative models of firm dynamics. Copyright (c) 2017 John Wiley & Sons, Ltd.

关键词： duration models finite mixtures em algorithm firm exit selection

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A Novel Social Event Organization Approach for Diverse User Choices

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COMPUTER JOURNAL 2017年第7期60卷 1078-1095页

作者： Huang, Jianbin Zhou, Yu Jia, Xiaolin Sun, Heli Xidian Univ Sch Software Xian Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Sch Software Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China

The boom of social networking services makes it convenient to organize or participate in social events. Recent studies consider the social event organization problem, but they cannot provide diverse choices for users when there are multiple events. In this paper, we seek to devise an event organization scheme that provides diverse choices for users. For this goal, we explicitly distinguish the subjective preferences of users to events and the objective preferences of users to events. The former is considered to be the generative probabilities of users appearing in events, and the latter is viewed as the posterior probabilities of users participating in events. The Expectation-Maximization algorithm is employed to connect them together. After extracting these features, we extract an edge-weighted bipartite graph as the scheme from all the objective preferences. It captures the maximum sum of the objective preferences, and simultaneously satisfies soft user capacity constraints and hard event capacity constraints. We prove this problem is in P via transforming it into linear program (LP). In consideration that LP may have no feasible solutions, we alternatively devise an efficient polynomial time algorithm which can yield an approximately feasible solution. Experimental evaluation shows the effectiveness and efficiency of our proposed method.

关键词： social event organization ranking em algorithm linear program

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Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第21期46卷 10879-10896页

作者： Dogru, Fatma Zehra Arslan, Olcay Giresun Univ Dept Econometr Fac Econ & Adm Sci Giresun Turkey Ankara Univ Dept Stat Fac Sci Ankara Turkey

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation-maximization (em) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the em algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.

关键词： em algorithm mixture model mixture regression model ML SLN STN 62F35 62H12 65C20 68U20

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