**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**2478

# Search results for: regression model

##### 2478 Model-Based Software Regression Test Suite Reduction

**Authors:**
Shiwei Deng,
Yang Bao

**Abstract:**

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

##### 2477 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.

##### 2476 Estimating Regression Parameters in Linear Regression Model with a Censored Response Variable

**Authors:**
Jesus Orbe,
Vicente Nunez-Anton

**Abstract:**

In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. In this context, most of the studies in the literature can be located in two possible general classes of regression models: models that study the effect the covariates have on the hazard function; and models that study the effect the covariates have on the censored response variable. Proposals in this paper are in the second class of models and, more specifically, on least squares based model approach. Thus, using the bootstrap estimate of the bias, we try to improve the estimation of the regression parameters by reducing their bias, for small sample sizes. Simulation results presented in the paper show that, for reasonable sample sizes and censoring levels, the bias is always smaller for the new proposals.

**Keywords:**
Censored response variable,
regression,
bias.

##### 2475 Speaker Independent Quranic Recognizer Basedon Maximum Likelihood Linear Regression

**Authors:**
Ehab Mourtaga,
Ahmad Sharieh,
Mousa Abdallah

**Abstract:**

**Keywords:**
Hidden Markov Model (HMM),
MaximumLikelihood Linear Regression (MLLR),
Quran,
Regression ClassTree,
Speech Recognition,
Speaker-independent.

##### 2474 Orthogonal Regression for Nonparametric Estimation of Errors-in-Variables Models

**Authors:**
Anastasiia Yu. Timofeeva

**Abstract:**

Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

**Keywords:**
Grade point average,
orthogonal regression,
penalized regression spline,
locally weighted regression.

##### 2473 Clustering Protein Sequences with Tailored General Regression Model Technique

**Authors:**
G. Lavanya Devi,
Allam Appa Rao,
A. Damodaram,
GR Sridhar,
G. Jaya Suma

**Abstract:**

**Keywords:**
Clustering,
General Regression Model,
Protein
Sequences,
Similarity Measure.

##### 2472 Analyzing the Factors Influencing Exclusive Breastfeeding Using the Generalized Poisson Regression Model

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

**Abstract:**

Exclusive breastfeeding is the feeding of a baby on no other milk apart from breast milk. Exclusive breastfeeding during the first 6 months of life is of fundamental importance because it supports optimal growth and development during infancy and reduces the risk of obliterating diseases and problems. Moreover, in developed countries, exclusive breastfeeding has decreased the incidence and/or severity of diarrhea, lower respiratory infection and urinary tract infection. In this paper, we study the factors that influence exclusive breastfeeding and use the Generalized Poisson regression model to analyze the practices of exclusive breastfeeding in Mauritius. We develop two sets of quasi-likelihood equations (QLE)to estimate the parameters.

**Keywords:**
Exclusive breastfeeding,
Regression model,
Quasilikelihood.

##### 2471 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

##### 2470 A General Regression Test Selection Technique

**Authors:**
Walid S. Abd El-hamid,
Sherif S. El-etriby,
Mohiy M. Hadhoud

**Abstract:**

**Keywords:**
Regression testing,
Model based testing,
Dynamicbehavior.

##### 2469 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.

##### 2468 Second Order Admissibilities in Multi-parameter Logistic Regression Model

**Authors:**
Chie Obayashi,
Hidekazu Tanaka,
Yoshiji Takagi

**Abstract:**

**Keywords:**
Berkson estimator,
modified maximum likelihood estimator,
Multi-parameter logistic regression model,
second order
admissibility.

##### 2467 Multiple Regression based Graphical Modeling for Images

**Authors:**
Pavan S.,
Sridhar G.,
Sridhar V.

**Abstract:**

Super resolution is one of the commonly referred inference problems in computer vision. In the case of images, this problem is generally addressed using a graphical model framework wherein each node represents a portion of the image and the edges between the nodes represent the statistical dependencies. However, the large dimensionality of images along with the large number of possible states for a node makes the inference problem computationally intractable. In this paper, we propose a representation wherein each node can be represented as acombination of multiple regression functions. The proposed approach achieves a tradeoff between the computational complexity and inference accuracy by varying the number of regression functions for a node.

**Keywords:**
Belief propagation,
Graphical model,
Regression,
Super resolution.

##### 2466 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.

##### 2465 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.

##### 2464 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.

##### 2463 Institutional Efficiency of Commonhold Industrial Parks Using a Polynomial Regression Model

**Authors:**
Jeng-Wen Lin,
Simon Chien-Yuan Chen

**Abstract:**

**Keywords:**
Homeowners Associations,
Institutional Efficiency,
Polynomial Regression,
Transaction Cost.

##### 2462 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.

##### 2461 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.

##### 2460 Performance Analysis of Adaptive LMS Filter through Regression Analysis using SystemC

**Authors:**
Hyeong-Geon Lee,
Jae-Young Park,
Suk-ki Lee,
Jong-Tae Kim

**Abstract:**

The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.

**Keywords:**
System level model,
adaptive LMS FIR filter,
regression analysis,
systemC.

##### 2459 Efficient System for Speech Recognition using General Regression Neural Network

**Authors:**
Abderrahmane Amrouche,
Jean Michel Rouvaen

**Abstract:**

**Keywords:**
Speech Recognition,
General Regression NeuralNetwork,
Hidden Markov Model,
Recurrent Neural Network,
ArabicDigits.

##### 2458 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.

##### 2457 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.

##### 2456 A Martingale Residual Diagnostic for Logistic Regression Model

**Authors:**
Entisar A. Elgmati

**Abstract:**

Martingale model diagnostic for assessing the fit of logistic regression model to recurrent events data are studied. One way of assessing the fit is by plotting the empirical standard deviation of the standardized martingale residual processes. Here we used another diagnostic plot based on martingale residual covariance. We investigated the plot performance under several types of model misspecification. Clearly the method has correctly picked up the wrong model. Also we present a test statistic that supplement the inspection of the two diagnostic. The test statistic power agrees with what we have seen in the plots of the estimated martingale covariance.

**Keywords:**
Covariance,
logistic model,
misspecification,
recurrent events.

##### 2455 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.

##### 2454 Factors for Entry Timing Choices Using Principal Axis Factorial Analysis and Logistic Regression Model

**Authors:**
Mat Isa,
C. M.,
Mohd Saman,
H.,
Mohd Nasir,
S. R.,
Jaapar,
A.

**Abstract:**

International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.

**Keywords:**
Factors,
early movers,
entry timing choices,
late movers,
Logistic Regression Model,
Principal Axis Factorial Analysis,
Malaysian construction firms.

##### 2453 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

##### 2452 Internet Purchases in European Union Countries: Multiple Linear Regression Approach

**Authors:**
Ksenija Dumičić,
Anita Čeh Časni,
Irena Palić

**Abstract:**

This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product *per capita *(GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.

**Keywords:**
European Union,
Internet purchases,
multiple linear regression model,
outlier

##### 2451 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.

##### 2450 Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco

**Authors:**
S. Benchelha,
H. C. Aoudjehane,
M. Hakdaoui,
R. El Hamdouni,
H. Mansouri,
T. Benchelha,
M. Layelmam,
M. Alaoui

**Abstract:**

The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

**Keywords:**
Landslide susceptibility mapping,
regression logistic,
multivariate adaptive regression spline,
Oudka,
Taounate,
Morocco.

##### 2449 A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets

**Authors:**
O. Poleshchuk,
E.Komarov

**Abstract:**

This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

**Keywords:**
Interval type-2 fuzzy sets,
fuzzy regression,
weighted interval.