**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**8273

# Search results for: Negative Binomial Regression model

##### 8273 Moment Estimators of the Parameters of Zero-One Inflated Negative Binomial Distribution

**Authors:**
Rafid Saeed Abdulrazak Alshkaki

**Abstract:**

**Keywords:**
Zero one inflated models,
negative binomial distribution,
moments estimator,
non-negative integer sampling.

##### 8272 Modelling Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) Outbreak Using Poisson and Negative Binomial Model

**Authors:**
W. Y. Wan Fairos,
W. H. Wan Azaki,
L. Mohamad Alias,
Y. Bee Wah

**Abstract:**

**Keywords:**
Dengue Fever,
Dengue Hemorrhagic Fever,
Negative Binomial Regression model,
Poisson Regression model.

##### 8271 A Study on Exclusive Breastfeeding using Over-dispersed Statistical Models

**Authors:**
Naushad Mamode Khan,
Cheika Jahangeer,
Maleika Heenaye-Mamode Khan

**Abstract:**

Breastfeeding is an important concept in the maternal life of a woman. In this paper, we focus on exclusive breastfeeding. Exclusive breastfeeding is the feeding of a baby on no other milk apart from breast milk. This type of breastfeeding is very important during the first six months because it supports optimal growth and development during infancy and reduces the risk of obliterating diseases and problems. Moreover, in Mauritius, exclusive breastfeeding has decreased the incidence and/or severity of diarrhea, lower respiratory infection and urinary tract infection. In this paper, we give an overview of exclusive breastfeeding in Mauritius and the factors influencing it. We further analyze the local practices of exclusive breastfeeding using the Generalized Poisson regression model and the negative-binomial model since the data are over-dispersed.

**Keywords:**
Exclusive breast feeding,
regression model,
generalized poisson,
negative binomial.

##### 8270 Fuzzy Logic Approach to Robust Regression Models of Uncertain Medical Categories

**Authors:**
Arkady Bolotin

**Abstract:**

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknown and can be specified only approximately, i.e. surrounded by some (small) uncertainty. It means that in order to have any practical meaning the regression model must be robust to this uncertainty. In this paper, we show that neither the beta in the linear regression model, nor its significance level is robust to the small variations in the dichotomization cut-off point. As an alternative robust approach to the problem of uncertain medical categories, we propose to use the linear regression model with the fuzzy membership function as a dependent variable. This fuzzy membership function denotes to what degree the value of the underlying (continuous) outcome falls below or above the dichotomization cut-off point. In the paper, we demonstrate that the linear regression model of the fuzzy dependent variable can be insensitive against the uncertainty in the cut-off point location. In the paper we present the modeling results from the real study of low hemoglobin levels in infants. We systematically test the robustness of the binomial regression model and the linear regression model with the fuzzy dependent variable by changing the boundary for the category Anemia and show that the behavior of the latter model persists over a quite wide interval.

**Keywords:**
Categorization,
Uncertain medical categories,
Binomial regression model,
Fuzzy dependent variable,
Robustness.

##### 8269 Air Pollution and Respiratory-Related Restricted Activity Days in Tunisia

**Authors:**
Mokhtar Kouki Inès Rekik

**Abstract:**

This paper focuses on the assessment of the air pollution and morbidity relationship in Tunisia. Air pollution is measured by ozone air concentration and the morbidity is measured by the number of respiratory-related restricted activity days during the 2-week period prior to the interview. Socioeconomic data are also collected in order to adjust for any confounding covariates. Our sample is composed by 407 Tunisian respondents; 44.7% are women, the average age is 35.2, near 69% are living in a house built after 1980, and 27.8% have reported at least one day of respiratory-related restricted activity. The model consists on the regression of the number of respiratory-related restricted activity days on the air quality measure and the socioeconomic covariates. In order to correct for zero-inflation and heterogeneity, we estimate several models (Poisson, negative binomial, zero inflated Poisson, Poisson hurdle, negative binomial hurdle and finite mixture Poisson models). Bootstrapping and post-stratification techniques are used in order to correct for any sample bias. According to the Akaike information criteria, the hurdle negative binomial model has the greatest goodness of fit. The main result indicates that, after adjusting for socioeconomic data, the ozone concentration increases the probability of positive number of restricted activity days.

**Keywords:**
Bootstrapping,
hurdle negbin model,
overdispersion,
ozone concentration,
respiratory-related restricted activity days.

##### 8268 Time Series Forecasting Using a Hybrid RBF Neural Network and AR Model Based On Binomial Smoothing

**Authors:**
Fengxia Zheng,
Shouming Zhong

**Abstract:**

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

**Keywords:**
Binomial smoothing (BS),
hybrid,
Canadian Lynx data,
forecasting accuracy.

##### 8267 Density Estimation using Generalized Linear Model and a Linear Combination of Gaussians

**Authors:**
Aly Farag,
Ayman El-Baz,
Refaat Mohamed

**Abstract:**

In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.

**Keywords:**
Logistic regression model,
Expectationmaximization,
Segmentation.

##### 8266 Using Artificial Neural Network to Predict Collisions on Horizontal Tangents of 3D Two-Lane Highways

**Authors:**
Omer F. Cansiz,
Said M. Easa

**Abstract:**

**Keywords:**
Collision frequency,
horizontal tangent,
3D two-lane
highway,
negative binomial,
zero inflated Poisson,
artificial neural
network.

##### 8265 Child Homicide Victimization and Community Context: A Research Note

**Authors:**
Bohsiu Wu

**Abstract:**

Among serious crimes, child homicide is a rather rare event. However, the killing of children stirs up a special type of emotion in society that pales other criminal acts. This study examines the relevancy of three possible community-level explanations for child homicide: social deprivation, female empowerment, and social isolation. The social deprivation hypothesis posits that child homicide results from lack of resources in communities. The female empowerment hypothesis argues that a higher female status translates into a higher level of capability to prevent child homicide. Finally, the social isolation hypothesis regards child homicide as a result of lack of social connectivity. Child homicide data, aggregated by US postal ZIP codes in California from 1990 to 1999, were analyzed with a negative binomial regression. The results of the negative binomial analysis demonstrate that social deprivation is the most salient and consistent predictor among all other factors in explaining child homicide victimization at the ZIP-code level. Both social isolation and female labor force participation are weak predictors of child homicide victimization across communities. Further, results from the negative binomial regression show that it is the communities with a higher, not lower, degree of female labor force participation that are associated with a higher count of child homicide. It is possible that poor communities with a higher level of female employment have a lesser capacity to provide the necessary care and protection for the children. Policies aiming at reducing social deprivation and strengthening female empowerment possess the potential to reduce child homicide in the community.

**Keywords:**
Child homicide,
deprivation,
empowerment,
isolation.

##### 8264 A Comparison of the Sum of Squares in Linear and Partial Linear Regression Models

**Authors:**
Dursun Aydın

**Abstract:**

**Keywords:**
Partial Linear Regression Model,
Linear RegressionModel,
Residuals,
Deviance,
Smoothing Spline.

##### 8263 Relationship between Sums of Squares in Linear Regression and Semi-parametric Regression

**Authors:**
Dursun Aydın,
Bilgin Senel

**Abstract:**

**Keywords:**
Semi-parametric regression,
Penalized LeastSquares,
Residuals,
Deviance,
Smoothing Spline.

##### 8262 Stock Market Prediction by Regression Model with Social Moods

**Authors:**
Masahiro Ohmura,
Koh Kakusho,
Takeshi Okadome

**Abstract:**

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

**Keywords:**
Regression model,
social mood,
stock market
prediction,
Twitter.

##### 8261 Zero Truncated Strict Arcsine Model

**Authors:**
Y. N. Phang,
E. F. Loh

**Abstract:**

The zero truncated model is usually used in modeling count data without zero. It is the opposite of zero inflated model. Zero truncated Poisson and zero truncated negative binomial models are discussed and used by some researchers in analyzing the abundance of rare species and hospital stay. Zero truncated models are used as the base in developing hurdle models. In this study, we developed a new model, the zero truncated strict arcsine model, which can be used as an alternative model in modeling count data without zero and with extra variation. Two simulated and one real life data sets are used and fitted into this developed model. The results show that the model provides a good fit to the data. Maximum likelihood estimation method is used in estimating the parameters.

**Keywords:**
Hurdle models,
maximum likelihood estimation
method,
positive count data.

##### 8260 Study on the Effect of Road Infrastructure, Socio-Economic and Demographic Features on Road Crashes in Bangladesh

**Authors:**
Shakil M. Rifaat,
Md. H. Rahman,
Mohammed,
Mosabbir Pasha

**Abstract:**

Road crashes not only claim lives and inflict injuries but also create economic burden to the society due to loss of productivity. The problem of deaths and injuries as a result of road traffic crashes is now acknowledged to be a global phenomenon with authorities in virtually all countries of the world concerned about the growth in the number of people killed and seriously injured on their roads. However, the road crash scenario of a developing country like Bangladesh is much worse comparing with this of developed countries. For developing proper countermeasures it is necessary to identify the factors affecting crash occurrences. The objectives of the study is to examine the effect of district wise road infrastructure, socioeconomic and demographic features on crash occurrence .The unit of analysis will be taken as individual district which has not been explored much in the past. Reported crash data obtained from Bangladesh Road Transport Authority (BRTA) from the year 2004 to 2010 are utilized to develop negative binomial model. The model result will reveal the effect of road length (both paved and unpaved), road infrastructure and several socio economic characteristics on district level crash frequency in Bangladesh.

**Keywords:**
Demographic,
Negative Binomial Model,
Road Infrastructure,
Socio-economic,
Traffic Safety.

##### 8259 Model-Based Software Regression Test Suite Reduction

**Authors:**
Shiwei Deng,
Yang Bao

**Abstract:**

**Keywords:**
Dependence analysis,
EFSM model,
greedy
algorithm,
regression test.

##### 8258 The Study of the Discrete Risk Model with Random Income

**Authors:**
Peichen Zhao

**Abstract:**

In this paper, we extend the compound binomial model to the case where the premium income process, based on a binomial process, is no longer a linear function. First, a mathematically recursive formula is derived for non ruin probability, and then, we examine the expected discounted penalty function, satisfy a defect renewal equation. Third, the asymptotic estimate for the expected discounted penalty function is then given. Finally, we give two examples of ruin quantities to illustrate applications of the recursive formula and the asymptotic estimate for penalty function.

**Keywords:**
Discounted penalty function,
compound binomial process,
recursive formula,
discrete renewal equation,
asymptotic estimate.

##### 8257 Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm

**Authors:**
Suparman

**Abstract:**

Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

**Keywords:**
Piecewise,
Bayesian,
reversible jump MCMC,
segmentation.

##### 8256 Survival Model for Partly Interval-Censored Data with Application to Anti D in Rhesus D Negative Studies

**Authors:**
F. A. M. Elfaki,
Amar Abobakar,
M. Azram,
M. Usman

**Abstract:**

This paper discusses regression analysis of partly interval-censored failure time data, which is occur in many fields including demographical, epidemiological, financial, medical and sociological studies. For the problem, we focus on the situation where the survival time of interest can be described by the additive hazards model in the present of partly interval-censored. A major advantage of the approach is its simplicity and it can be easily implemented by using R software. Simulation studies are conducted which indicate that the approach performs well for practical situations and comparable to the existing methods. The methodology is applied to a set of partly interval-censored failure time data arising from anti D in Rhesus D negative studies.

**Keywords:**
Anti D in Rhesus D negative,
Cox’s model,
EM algorithm.

##### 8255 Zero Inflated Strict Arcsine Regression Model

**Authors:**
Y. N. Phang,
E. F. Loh

**Abstract:**

**Keywords:**
Overdispersed count data,
maximum likelihood
estimation,
simulated annealing.

##### 8254 A Fuzzy Linear Regression Model Based on Dissemblance Index

**Authors:**
Shih-Pin Chen,
Shih-Syuan You

**Abstract:**

**Keywords:**
Dissemblance index,
fuzzy linear regression,
graded
mean integration,
mathematical programming.

##### 8253 Quality of Service Evaluation using a Combination of Fuzzy C-Means and Regression Model

**Authors:**
Aboagela Dogman,
Reza Saatchi,
Samir Al-Khayatt

**Abstract:**

**Keywords:**
Fuzzy C-means; regression model,
network quality
of service

##### 8252 Research on the Problems of Housing Prices in Qingdao from a Macro Perspective

**Authors:**
Liu Zhiyuan,
Sun Zongdi,
Liu Zhiyuan,
Sun Zongdi

**Abstract:**

Qingdao is a seaside city. Taking into account the characteristics of Qingdao, this article established a multiple linear regression model to analyze the impact of macroeconomic factors on housing prices. We used stepwise regression method to make multiple linear regression analysis, and made statistical analysis of F test values and T test values. According to the analysis results, the model is continuously optimized. Finally, this article obtained the multiple linear regression equation and the influencing factors, and the reliability of the model was verified by F test and T test.

**Keywords:**
Housing prices,
multiple linear regression model,
macroeconomic factors,
Qingdao City.

##### 8251 Neuro-fuzzy Model and Regression Model a Comparison Study of MRR in Electrical Discharge Machining of D2 Tool Steel

**Authors:**
M. K. Pradhan,
C. K. Biswas,

**Abstract:**

In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively

**Keywords:**
Electrical discharge machining,
material removal rate,
neuro-fuzzy model,
regression model,
mountain clustering.

##### 8250 A Hybrid Model of ARIMA and Multiple Polynomial Regression for Uncertainties Modeling of a Serial Production Line

**Authors:**
Amir Azizi,
Amir Yazid b. Ali,
Loh Wei Ping,
Mohsen Mohammadzadeh

**Abstract:**

**Keywords:**
ARIMA,
multiple polynomial regression,
production
throughput,
uncertainties

##### 8249 Development of Regression Equation for Surface Finish and Analysis of Surface Integrity in EDM

**Authors:**
Md. Ashikur Rahman Khan,
M. M. Rahman

**Abstract:**

Electrical discharge machining (EDM) is a relatively modern machining process having distinct advantages over other machining processes and can machine Ti-alloys effectively. The present study emphasizes the features of the development of regression equation based on response surface methodology (RSM) for correlating the interactive and higher-order influences of machining parameters on surface finish of Titanium alloy Ti-6Al-4V. The process parameters selected in this study are discharge current, pulse on time, pulse off time and servo voltage. Machining has been accomplished using negative polarity of Graphite electrode. Analysis of variance is employed to ascertain the adequacy of the developed regression model. Experiments based on central composite of response surface method are carried out. Scanning electron microscopy (SEM) analysis was performed to investigate the surface topography of the EDMed job. The results evidence that the proposed regression equation can predict the surface roughness effectively. The lower ampere and short pulse on time yield better surface finish.

**Keywords:**
Graphite electrode,
regression model,
response surface methodology,
surface roughness.

##### 8248 The Maximum Likelihood Method of Random Coefficient Dynamic Regression Model

**Authors:**
Autcha Araveeporn

**Abstract:**

**Keywords:**
Autoregressive,
Maximum Likelihood Method,
Nonstationarity,
Random Coefficient Dynamic Regression,
Stationary.

##### 8247 Robust Regression and its Application in Financial Data Analysis

**Authors:**
Mansoor Momeni,
Mahmoud Dehghan Nayeri,
Ali Faal Ghayoumi,
Hoda Ghorbani

**Abstract:**

This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.

**Keywords:**
Financial data analysis,
Influential data,
Outliers,
Robust regression.

##### 8246 Choosing between the Regression Correlation, the Rank Correlation, and the Correlation Curve

**Authors:**
Roger L Goodwin

**Abstract:**

**Keywords:**
Bayesian estimation,
regression model,
rank
statistics,
correlation,
correlation curve.

##### 8245 The Relative Efficiency of Parameter Estimation in Linear Weighted Regression

**Authors:**
Baoguang Tian,
Nan Chen

**Abstract:**

A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.

**Keywords:**
Linear weighted regression,
Relative efficiency,
Mean matrix,
Trace.

##### 8244 Optimized Calculation of Hourly Price Forward Curve (HPFC)

**Authors:**
Ahmed Abdolkhalig

**Abstract:**

**Keywords:**
Forward curve,
furrier series,
regression,
radial basic
function neural networks.