A hierarchical Bayesian model for inference of copy number variants and their association to gene expression

A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS705 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A User\u27s Manual for Computer Programs Used in: Model Choice: An Operational Comparison of Stochastic Streamflow Models for Droughts

The rapid development of stochastic or operational hydrology over the past 10 years has led to the need for some comparative analyses of the currently available long-term persistence models. Five annual stochastic streamflow generation models (autoregressive, autoregressive-moviing-average (ARMA), ARMA-Markov, fast fractional Gaussian noise, and broken line) are compared on their ability to preserved drought-related time series properties and annual statistics. Using Monto Carlo generation procedures and comparing the average generated statistics and drought or water supply properties, a basis is established to evaluated model performance on four different Utah study streams. A seasonal disaggregation model is applied to each of the generated annual models for each of the four study streams at a monthly disaggregation level. A model choice strategy is presented for the water resources engineer to select an annual stochastic streamflow model based on values of the historic time series; lag-one serial correlation and Hurst coefficient. Procedures are presented for annual and seasonal model parameter estimation, calibration, and generation. Techniques to ensure a consistent matrix for successful matric decomposition are included such as normality, trend-analysis, and choice of model. User oriented model parameter estimation techniques that are easy and efficient to use are presented in a systematic manner. The ARMA-Markov and ARMA models are judged to be the best overall models in terms of preserving the short and long term persistence statistics for the four historic time series studied. The broken line model is judged to be the best model in terms of minimizing the economic regret as determined by an agricultural crop production function. Documentation and listings of the computer programs that were used for the stochastic models\u27 parameter estimation, generation, and comparison techniques are presente in a supplementary appendix

Model choice: An Operational Comparison of Stochastic Streamflow Models for Droughts

The rapid development of stochastic or operational hydrology over the past 10 years has led to the need for some comparative analyses of the currently available long-term persistence models. Five annual stochastic streamflow generation models (autoregressive, autoregressive-moving-average (ARMA), ARMA-Markov, fast fractional Gaussian noise, and broken line) are compared on their ability to preserve drought-related time series properties and annual statistics. Using Monte Carlo generation procedures and comparing the average generated statistics and drought or water supply properties, a basis is established to evalute model performance on four different Utah study streams. A seasonal disaggregation model is applied to each of the generated annual models for each of the four study streams at a monthly disaggregation level. A model choice strategy is presented for the water resources engineer to select an annual stochastic streamflow model based on values of the historic time series\u27 lag-one serial correlation and Hurst coefficient. Procedures are presented for annual and seasonal model parameter estimatino, calibration, and generation. Techniques are included such as normality, trend-analysis, and choice of model. User oriented model parameter estimation techniques that are easy and efficient to use are presented in a systematic manner. The ARMA-Markov and ARMA models are judged to be the best overall models in terms of preserving the short and long term persistence statistics for the four historic time series studied. The broken line model is judged to be the best model in terms of minimizing the evonomic regret as determined by an agricultural crop production function. Documentation and listings of the computer programs that were used for the stochastic models\u27 parameter estimation, generation, and camparison techniques are presented in a supplementary appendix

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm

A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Mean Field Equilibrium in Dynamic Games with Complementarities

We study a class of stochastic dynamic games that exhibit strategic complementarities between players; formally, in the games we consider, the payoff of a player has increasing differences between her own state and the empirical distribution of the states of other players. Such games can be used to model a diverse set of applications, including network security models, recommender systems, and dynamic search in markets. Stochastic games are generally difficult to analyze, and these difficulties are only exacerbated when the number of players is large (as might be the case in the preceding examples). We consider an approximation methodology called mean field equilibrium to study these games. In such an equilibrium, each player reacts to only the long run average state of other players. We find necessary conditions for the existence of a mean field equilibrium in such games. Furthermore, as a simple consequence of this existence theorem, we obtain several natural monotonicity properties. We show that there exist a "largest" and a "smallest" equilibrium among all those where the equilibrium strategy used by a player is nondecreasing, and we also show that players converge to each of these equilibria via natural myopic learning dynamics; as we argue, these dynamics are more reasonable than the standard best response dynamics. We also provide sensitivity results, where we quantify how the equilibria of such games move in response to changes in parameters of the game (e.g., the introduction of incentives to players).Comment: 56 pages, 5 figure

The Use of Parametric and Non Parametric Frontier Methods to Measure the Productive Efficiency in the Industrial Sector. A Comparative Study

Parametric frontier models and non-parametric methods have monopolised the recent literature on productive efficiency measurement. Empirical applications have usually dealt with either one or the other group of techniques. This paper applies a range of both types of approaches to an industrial organisation setup. The joint use can improve the accuracy of both, although some methodological difficulties can arise. The robustness of different methods in ranking productive units allows us to make an comparative analysis of them. Empirical results concern productive and market demand structure, returns-to-scale, and productive inefficiency sources. The techniques are illustrated using data from the US electric power industry.Productive efficiency; parametric frontiers; DEA; industrial sector

Technical Efficiency in Organic Farming: an Application on Italian Cereal Farms using a Parametric Approach

A stochastic frontier production model was applied to estimate technical efficiency in a sample of Italian organic and conventional cereal farms. The main purpose was to assess which production technique revealed higher efficiency. Statistical tests on the pool sample model suggested that differences between the two cultivation methods were significant from a technological viewpoint. Separate analyses of two sub-samples (93 and 138 observations for organic and conventional farms, respectively) found that conventional farms were significantly more efficient than organic farms, with respect to their specific technology (0.892 vs. 0.825). This implies that organic (conventional) cereal farmers could increase their income to 99.19 €/ha (40.95 €/ha). Analysis also estimated that land was the technical input with the highest elasticity for both technologies. Furthermore, findings indicated that 63.7% of the differentials between observed and best-practice output was explained by technical inefficiency for the conventional group, while this value was close to unity for organic farms. Some policy implications can be drawn from these findings