**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**238

# Search results for: regression

##### 238 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.

##### 237 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.

##### 236 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.

##### 235 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.

##### 234 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.

##### 233 Comparison of Multivariate Adaptive Regression Splines and Random Forest Regression in Predicting Forced Expiratory Volume in One Second

**Authors:**
P. V. Pramila,
V. Mahesh

**Abstract:**

Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients resulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects) with the aforementioned input features. It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, as well as yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

**Keywords:**
FEV1,
Multivariate Adaptive Regression Splines
Pulmonary Function Test,
Random Forest.

##### 232 A Comparison of Some Thresholding Selection Methods for Wavelet Regression

**Authors:**
Alsaidi M. Altaher,
Mohd T. Ismail

**Abstract:**

In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many coefficients resulting in over smoothing. Conversely, a too small threshold value allows many coefficients to be included in reconstruction, giving a wiggly estimate which result in under smoothing. However, the proper choice of threshold can be considered as a careful balance of these principles. This paper gives a very brief introduction to some thresholding selection methods. These methods include: Universal, Sure, Ebays, Two fold cross validation and level dependent cross validation. A simulation study on a variety of sample sizes, test functions, signal-to-noise ratios is conducted to compare their numerical performances using three different noise structures. For Gaussian noise, EBayes outperforms in all cases for all used functions while Two fold cross validation provides the best results in the case of long tail noise. For large values of signal-to-noise ratios, level dependent cross validation works well under correlated noises case. As expected, increasing both sample size and level of signal to noise ratio, increases estimation efficiency.

**Keywords:**
wavelet regression,
simulation,
Threshold.

##### 231 A Robust LS-SVM Regression

**Authors:**
József Valyon,
Gábor Horváth

**Abstract:**

**Keywords:**
Support Vector Machines,
Least Squares SupportVector Machines,
Regression,
Sparse approximation.

##### 230 Multivariate School Travel Demand Regression Based on Trip Attraction

**Authors:**
Ben-Edigbe J,
RahmanR

**Abstract:**

**Keywords:**
Trip generation,
regression analysis,
multiple linearregressions

##### 229 Ensembling Adaptively Constructed Polynomial Regression Models

**Authors:**
Gints Jekabsons

**Abstract:**

**Keywords:**
Basis function construction,
heuristic search,
modelensembles,
polynomial regression.

##### 228 A Comparison of the Nonparametric Regression Models using Smoothing Spline and Kernel Regression

**Authors:**
Dursun Aydin

**Abstract:**

**Keywords:**
Kernel regression,
Nonparametric models,
Prediction,
Smoothing spline.

##### 227 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.

##### 226 Estimation of Time -Varying Linear Regression with Unknown Time -Volatility via Continuous Generalization of the Akaike Information Criterion

**Authors:**
Elena Ezhova,
Vadim Mottl,
Olga Krasotkina

**Abstract:**

**Keywords:**
Time varying regression,
time-volatility of regression
coefficients,
Akaike Information Criterion (AIC),
Kullback information
maximization principle.

##### 225 Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting

**Authors:**
R. Behmanesh,
I. Rahimi

**Abstract:**

**Keywords:**
RNN,
DOE,
regression,
control chart.

##### 224 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.

##### 223 On the outlier Detection in Nonlinear Regression

**Authors:**
Hossein Riazoshams,
Midi Habshah,
Jr.,
Mohamad Bakri Adam

**Abstract:**

**Keywords:**
Nonlinear Regression,
outliers,
Gradient,
LeastSquare,
M-estimate,
MM-estimate.

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

**Authors:**
Shiwei Deng,
Yang Bao

**Abstract:**

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

##### 221 Defect Cause Modeling with Decision Tree and Regression Analysis

**Authors:**
B. Bakır,
İ. Batmaz,
F. A. Güntürkün,
İ. A. İpekçi,
G. Köksal,
N. E. Özdemirel

**Abstract:**

**Keywords:**
Casting industry,
decision tree algorithm C5.0,
logistic regression,
quality improvement.

##### 220 Predictive Clustering Hybrid Regression(pCHR) Approach and Its Application to Sucrose-Based Biohydrogen Production

**Authors:**
Nikhil,
Ari Visa,
Chin-Chao Chen,
Chiu-Yue Lin,
Jaakko A. Puhakka,
Olli Yli-Harja

**Abstract:**

**Keywords:**
Biohydrogen,
bioprocess modeling,
clusteringhybrid regression.

##### 219 Optimization of Slider Crank Mechanism Using Design of Experiments and Multi-Linear Regression

**Authors:**
Galal Elkobrosy,
Amr M. Abdelrazek,
Bassuny M. Elsouhily,
Mohamed E. Khidr

**Abstract:**

Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^{rd} degree to 1^{st }degree and suggested valid predictions and stable explanations.

**Keywords:**
Design of experiments,
regression analysis,
SI Engine,
statistical modeling.

##### 218 Nuclear Fuel Safety Threshold Determined by Logistic Regression Plus Uncertainty

**Authors:**
D. S. Gomes,
A. T. Silva

**Abstract:**

**Keywords:**
Logistic regression,
reactivity-initiated accident,
safety margins,
uncertainty propagation.

##### 217 Two New Relative Efficiencies of Linear Weighted Regression

**Authors:**
Shuimiao Wan,
Chao Yuan,
Baoguang Tian

**Abstract:**

**Keywords:**
Linear weighted regression,
Relative efficiency,
Lower bound,
Parameter estimation.

##### 216 A Study on Inference from Distance Variables in Hedonic Regression

**Authors:**
Yan Wang,
Yasushi Asami,
Yukio Sadahiro

**Abstract:**

In urban area, several landmarks may affect housing price and rents, and hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

**Keywords:**
Landmarks,
hedonic regression,
distance variables,
collinearity,
multicollinerity.

##### 215 Dichotomous Logistic Regression with Leave-One-Out Validation

**Authors:**
Sin Yin Teh,
Abdul Rahman Othman,
Michael Boon Chong Khoo

**Abstract:**

**Keywords:**
Dichotomous logistic regression,
leave-one-out,
testof spread.

##### 214 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.

##### 213 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.

##### 212 Regression Test Selection Technique for Multi-Programming Language

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

**Abstract:**

**Keywords:**
Regression testing,
testing,
test selection,
softwareevolution,
software maintenance.

##### 211 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.

##### 210 A Comparative Study of Additive and Nonparametric Regression Estimators and Variable Selection Procedures

**Authors:**
Adriano Z. Zambom,
Preethi Ravikumar

**Abstract:**

**Keywords:**
Additive models,
local polynomial regression,
residuals,
mean square error,
variable selection.

##### 209 Computational Aspects of Regression Analysis of Interval Data

**Authors:**
Michal Cerny

**Abstract:**

We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.

**Keywords:**
Linear regression,
interval-censored data,
computational complexity.