Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Soft sensor for real-time cement fineness estimation

This paper describes the design and implementation of soft sensors to estimate cement fineness. Soft sensors are mathematical models that use available data to provide real-time information on process variables when the information, for whatever reason, is not available by direct measurement. In this application, soft sensors are used to provide information on process variable normally provided by off-line laboratory tests performed at large time intervals. Cement fineness is one of the crucial parameters that define the quality of produced cement. Providing real-time information on cement fineness using soft sensors can overcome limitations and problems that originate from a lack of information between two laboratory tests. The model inputs were selected from candidate process variables using an information theoretic approach. Models based on multi-layer perceptrons were developed, and their ability to estimate cement fineness of laboratory samples was analyzed. Models that had the best performance, and capacity to adopt changes in the cement grinding circuit were selected to implement soft sensors. Soft sensors were tested using data from a continuous cement production to demonstrate their use in real-time fineness estimation. Their performance was highly satisfactory, and the sensors proved to be capable of providing valuable information on cement grinding circuit performance. After successful off-line tests, soft sensors were implemented and installed in the control room of a cement factory. Results on the site confirm results obtained by tests conducted during soft sensor development.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

野外大气扩散试验中四面体气球示踪实验的初步评价

1985年11月在瑞士北部进行了一次以探讨“冷池”条件下的扩散和湍流特征为目的的国际野外大气试验,本文介绍其中的四面体气球示踪实验的结果。一共进行了三次由雷达跟踪的四面体示踪实验,释放了10个四面体球。按多轨迹法和单轨迹法分别估算并比较了其中两次示踪实验得到的水平扩散参数σ_y,探讨了风摆效应的贡献。采用“体源模式”并结合单轨迹法估算了微弱风场不定风向条件下的扩散参数,分析了静风,弱风条件下的水平风向标准差和湍流强度。     

Improving Cost Estimates of Construction Projects Using Phased Cost Factors

Central to cost-based competition is the capability to accurately predict the cost of delivering a project. Most literature on cost estimation focuses on specific estimation methods as generic techniques and little attention has been paid to the unique requirements at each project stage. This note attempts to identify the critical factors for effective estimation at various stages of typical construction projects. Drawing from organization control theory and cost estimating literature, this note develops a theoretical framework that identifies the critical factors for effective cost estimation during each project phase of a conventional construction project. The underlying logic is that as a cost estimating effort progresses, both task programmability and output measurability improve. As a result, control effort will shift from input-oriented control to a combination of output and behavior control.     

SECANT-FUZZY LINEAR REGRESSION METHOD FOR HARMONIC COMPONENTS ESTIMATION IN A POWER SYSTEM

In order to avoid unnecessary damage of electrical equipments and installations,high quality power should be delivered to the end user and strict control on frequency should be made, Therefore, it is important to estimate the power system's harmonic components with higher accuracy. This paper presents a new approach for estimating harmonic component in a power system using secant - fuzzy linear regression method. In this approach the non - sinusoidal voltage or current waveform is written as I linear function. The coefficient of this function is assumed to be fuzzy number with a membership function that has center and spread value. The time dependent quantity is written as Taylor series with two different time dependent quantities. The objective is to use the sample obtained from the transmission line to find the power system harmonic components and frequencies. We used an experimental voltage signal from a sub power station as a numerical test.     

A generalized gamma distribution with application to drought data

Gamma distributions are some of the most popular models for hydrological processes. In this paper, a very flexible family which contains the gamma distribution as a particular case is introduced. Evidence of flexibility is shown by examining the shape of its probability density function (pdf). A treatment of the mathematical properties is provided by deriving expressions for the n th moment. Estimation and simulation issues are also considered. Finally, a detailed application to drought data from the State of Nebraska is illustrated.     

Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method

The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a ? function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator (MLE), applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.     

Composite group vector space method for estimating melting and boiling point of pure organic compound

Composition Group Vector Space (CGVS) method for estimating melting and boiling point T _m , T _b of organic compound has been proposed, and the principle of this method has been elucidated. The models for estimating T _m , T _b have been established and the numerical values of relative parameters have been presented. The average percentage deviations of T _m , T _b estimation are 7.53 and 1.58, respectively, which show that the present method demonstrates significant improvement in applicability to predict the above properties, compared to conventional group methods.     

Kinetics of volume expansion during infusion of Ringer’s solution based on two volume model

In this work mathematical models were developed to represent the kinetics of volume changes of fluid spaces associated with infusion of Ringer’s solution. During infusion of Ringer’s solution, the human body is assumed to be characterized by the two-fluid space model which has second volume space in addition to the first volume so that fluid exchanges between these two spaces are possible. Various infusion types were tested to accommodate different medical situations. Volunteers were given Ringer’s solution and the changes in blood hemoglobin were detected. From the comparison with experimental data, the two-fluid space model was found to represent adequately the kinetics of human volume expansion during infusion of Ringer’s solution.