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The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling

PRODUCTION AND OPERATIONS MANAGemENT 2017年第10期26卷 1893-1914页

作者： Ranjan, Chitta Paynabar, Kamran Helm, Jonathan E. Pan, Julian Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Arizona State Univ WP Carey Sch Business Dept Supply Chain Management 300 E Lemon St Tempe AZ 85287 USA Lean Care Solut Corp Pte Ltd 28 Ayer Rajah Crescent 03-01 Singapore Singapore

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and long-standing problem in hospital management. The majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel semi-Markov model (SMM)-based clustering scheme, as opposed to clustering by patient attributes as in previous literature. Our methodology is validated by simulation and then applied to real patient data from a partner hospital where we demonstrate that it outperforms a suite of well-established clustering methods. Furthermore, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM-clustering is a novel approach applicable to any temporal-spatial stochastic data that is prevalent in many industries and application areas.

关键词： Clustering em algorithm semi-Markov mixture model patient flow estimation stochastic location models

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Spatial data fusion for large non-Gaussian remote sensing datasets

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STAT 2017年第1期6卷 390-404页

作者： Shi, Hongxiang Kang, emily L. Univ Cincinnati Dept Math Sci 4199 French Hall West Cincinnati OH 45221 USA

Remote sensing data are playing a vital role in understanding the pattern of the Earth's geophysical processes in environmental and climate sciences. We propose a spatial data-fusion methodology that is able to take advantage of two (or potentially more) large remote sensing datasets with the exponential family of distributions. Our hierarchical model follows the generalized linear mixed model but also leverages a low-rank spatial random effects model to allow for flexible spatial covariance and cross-covariance structure. We take an empirical hierarchical modelling approach where any unknown parameters are estimated by maximum likelihood estimation via an efficient expectation-maximization algorithm. Through a Markov chain Monte Carlo algorithm, spatial predictions are obtained by generating samples from the empirical predictive distribution where the unknown parameters are substituted by the estimates. The performance of our proposed method is investigated through a simulation study and a real-data example. It shows that via borrowing strength across complementary datasets, the proposed method improves spatial predictions reciprocally. Copyright (c) 2017 John Wiley & Sons, Ltd.

关键词： em algorithm empirical Bayes generalized linear mixed model hierarchical modelling multivariate geostatistics spatial random effects

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Modeling time series of counts with a new class of INAR(1) model

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STATISTICAL PAPERS 2017年第2期58卷 393-416页

作者： Khoo, Wooi Chen Ong, Seng Huat Biswas, Atanu Univ Malaya Inst Math Sci Fac Sci Kuala Lumpur 50603 Malaysia Indian Stat Inst Appl Stat Unit 203 BT Rd Kolkata 700108 India

This paper presents a new model for a stationary non-negative first order of integer-valued random variables based on the Pegram and thinning operators. Some fundamental and regression properties of the proposed model are discussed. Maximum likelihood estimation (MLE) by the em algorithm is applied to estimate the parameters. Numerical studies to compare the proposed model with the thinning and Pegram models and the breakdown point of MLE for the proposed model have been conducted. Finally, a real life count data set has been used to illustrate its application. Comparison with existing models by AIC showed that the proposed model is much better and illustrates its potential usefulness in empirical modeling.

关键词： Pegram operator Thinning operator Mixture em algorithm Robustness Additive and innovative outliers

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Water flow probabilistic predictions based on a rainfall-runoff simulator: a two-regime model with variable selection

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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017年第2期22卷 194-219页

作者： Courbariaux, Marie Barbillon, Pierre Parent, Eric Univ Paris Saclay INRA AgroParisTech UMR MIA Paris F-75005 Paris France

Probabilistic forecasting aims at producing a predictive distribution of the quantity of interest instead of a single best guess point-wise estimate. With regard to water flow forecasts, the two main sources of uncertainty stem from unknown future rainfall and temperature (input error, i.e., meteorological uncertainty) and from the inadequacy of the deterministic simulator mimicking the rainfall-runoff (RR) transformation (hydrological uncertainty or RR error). These two sources of uncertainty can be dealt with separately and only the latter will be considered here. Only hydrological uncertainty is at stake when recorded meteorological data (instead of meteorological forecasts) are used as inputs to feed the RR simulator (RRS) for probabilistic predictions. The predictive performance of the RRS may strongly depend on the hydrological regimes: rapid flood variations induce large errors of anticipation but a series of dry events will translate into a much more smoother sequence of river levels due to the easily predictable behavior of the soil reservoir emptying. Consequently, a model with several regimes adapted to different error structures appears as a solution to cope with the issue of unstationary predictive variance. The river regime is modeled as a latent variable, the distribution of which is based on additional outputs of the RRS to be selected. Inference is performed by the em algorithm with both steps leading to explicit analytic expressions. Asymptotic confidence regions for the estimates are provided within the same em framework. Model selection is also performed, including the length of the model memory as well as the choice of explanatory variables for the latent regimes. The model is applied to a series of water flow forecasts routinely issued by two hydroelectricity producers in France and in Qu,bec and compared with their present operational forecasting methods.

关键词： em algorithm Probit model Model uncertainty Probabilistic forecasts Hydrology Rainfall-runoff model

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Estimation Methods for the Generalized Inverted Exponential Distribution Under Type II Progressively Hybrid Censoring with Application to Spreading of Micro-Drops Data

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Communications in Mathematics and Statistics 2017年第2期5卷 159-174页

作者： Hanieh Panahi Department of Mathematics and Statistics Lahijan BranchIslamic Azad UniversityLahijanIran

In this article,we consider the statistical inferences of the unknown parameters of a generalized inverted exponential distribution based on the Type II progressively hybrid censored *** applying the expectation–maximization(em)algorithm,the maximum likelihood estimators are developed for estimating the unknown *** observed Fisher information matrix is obtained using the missing information principle,and it can be used for constructing asymptotic con-fidence *** applying the bootstrapping technique,the confidence intervals for the parameters are also *** estimates of the unknown parameters are obtained using the Lindley’s *** Carlo simulations are imple-mented and observations are ***,a real data set representing the spread factor of micro-drops is analyzed to illustrative purposes.

关键词： Bootstrap method em algorithm Generalized inverted exponential Lindley’s approximation Micro-drops Type II progressively hybrid censoring

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A Variational Maximization-Maximization algorithm for Generalized Linear Mixed Models with Crossed Random Effects

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PSYCHOMETRIKA 2017年第3期82卷 693-716页

作者： Jeon, Minjeong Rijmen, Frank Rabe-Hesketh, Sophia Univ Calif Los Angeles Los Angeles CA USA Amer Inst Res Washington DC USA Univ Calif Berkeley Berkeley CA 94720 USA

We present a variational maximization-maximization algorithm for approximate maximum likelihood estimation of generalized linear mixed models with crossed random effects (e.g., item response models with random items, random raters, or random occasion-specific effects). The method is based on a factorized variational approximation of the latent variable distribution given observed variables, which creates a lower bound of the log marginal likelihood. The lower bound is maximized with respect to the factorized distributions as well as model parameters. With the proposed algorithm, a high-dimensional intractable integration is translated into a two-dimensional integration problem. We incorporate an adaptive Gauss-Hermite quadrature method in conjunction with the variational method in order to increase computational efficiency. Numerical studies show that under the small sample size conditions that are considered the proposed algorithm outperforms the Laplace approximation.

关键词： variational approximation lower bound Kullback-Leibler divergence em algorithm VMM algorithm adaptive quadrature GLMM crossed random effects

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Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data

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BIOMETRIKA 2017年第3期104卷 505-525页

作者： Zeng, Donglin Gao, Fei Lin, D. Y. Univ N Carolina Dept Biostat CB 7420 Chapel Hill NC 27599 USA

Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes. We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we develop an em algorithm that converges stably for arbitrary datasets. Finally, we assess the performance of the proposed methods in extensive simulation studies and illustrate their application using data derived from the Atherosclerosis Risk in Communities Study.

关键词： Current-status data em algorithm Multivariate failure time data Nonparametric likelihood Profile likelihood Proportional hazards Proportional odds Random effects

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An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data

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STATISTICS IN MEDICINE 2017年第7期36卷 1157-1171页

作者： Zhou, Jie Zhang, Jiajia Lu, Wenbin Univ South Carolina Dept Epidemiol & Biostat 915 Greene StSuite 453 Columbia SC 29208 USA North Carolina State Univ Dept Stat Raleigh NC USA

The generalized odds-rate model is a class of semiparametric regression models, which includes the proportional hazards and proportional odds models as special cases. There are few works on estimation of the generalized odds-rate model with interval censored data because of the challenges in maximizing the complex likelihood function. In this paper, we propose a gamma-Poisson data augmentation approach to develop an Expectation Maximization algorithm, which can be used to fit the generalized odds-rate model to interval censored data. The proposed Expectation Maximization algorithm is easy to implement and is computationally efficient. The performance of the proposed method is evaluated by comprehensive simulation studies and illustrated through applications to datasets from breast cancer and hemophilia studies. In order to make the proposed method easy to use in practice, an R package ICGOR' was developed. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： data augmentation em algorithm interval censoring generalized odds-rate models

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Network analysis for count data with excess zeros

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BMC GENETICS 2017年第1期18卷 1-10页

作者： Choi, Hosik Gim, Jungsoo Won, Sungho Kim, You Jin Kwon, Sunghoon Park, Changyi Kyonggi Univ Dept Appl Stat Suwon 16227 South Korea Seoul Natl Univ Inst Hlth & Environm Seoul 08826 South Korea Seoul Natl Univ Grad Sch Publ Hlth Seoul 08826 South Korea Ewha Womans Univ Dept Nutr Sci & Food Management Seoul 03760 South Korea Konkuk Univ Dept Appl Stat Seoul 05029 South Korea Univ Seoul Dept Stat Seoul 02504 South Korea

Background: Undirected graphical models or Markov random fields have been a popular class of models for representing conditional dependence relationships between nodes. In particular, Markov networks help us to understand complex interactions between genes in biological processes of a cell. Local Poisson models seem to be promising in modeling positive as well as negative dependencies for count data. Furthermore, when zero counts are more frequent than are expected, excess zeros should be considered in the model. Methods: We present a penalized Poisson graphical model for zero inflated count data and derive an expectation-maximization (em) algorithm built on coordinate descent. Our method is shown to be effective through simulated and real data analysis. Results: Results from the simulated data indicate that our method outperforms the local Poisson graphical model in the presence of excess zeros. In an application to a RNA sequencing data, we also investigate the gender effect by comparing the estimated networks according to different genders. Our method may help us in identifying biological pathways linked to sex hormone regulation and thus understanding underlying mechanisms of the gender differences. Conclusions: We have presented a penalized version of zero inflated spatial Poisson regression and derive an efficient em algorithm built on coordinate descent. We discuss possible improvements of our method as well as potential research directions associated with our findings from the RNA sequencing data.

关键词： Count data em algorithm Network Zero inflation

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Automated liver segmentation from a postmortem CT scan based on a statistical shape model

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2017年第2期12卷 205-221页

作者： Saito, Atsushi Yamamoto, Seiji Nawano, Shigeru Shimizu, Akinobu Tokyo Univ Agr & Technol Koganei Tokyo Japan Ai Informat Ctr Chuo Ku Tokyo Japan Int Univ Hlth & Welf Mita Hosp Dept Radiol Minato Ku Tokyo Japan

Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver. The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (em) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label. The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference. We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided em algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.

关键词： Liver segmentation Postmortem CT Statistical shape model em algorithm Autopsy imaging Synthesized-based learning

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