**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**2204

# Search results for: Linear regression

##### 2204 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.

##### 2203 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.

##### 2202 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.

##### 2201 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

##### 2200 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.

##### 2199 Economic Dispatch Fuzzy Linear Regression and Optimization

**Authors:**
A. K. Al-Othman

**Abstract:**

**Keywords:**
Economic Dispatch,
Fuzzy Linear Regression (FLP)and Optimization.

##### 2198 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.

##### 2197 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.

##### 2196 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.

##### 2195 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.

##### 2194 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.

##### 2193 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.

##### 2192 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.

##### 2191 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.

##### 2190 Multi-Linear Regression Based Prediction of Mass Transfer by Multiple Plunging Jets

**Abstract:**

The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90^{O}; whereas, multiple inclined plunging jets have jet impact angle of θ = 60^{O}. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (K_{L}a) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.

**Keywords:**
Mass transfer,
multiple plunging jets,
multi-linear regression.

##### 2189 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.

##### 2188 Harmonics Elimination in Multilevel Inverter Using Linear Fuzzy Regression

**Authors:**
A. K. Al-Othman,
H. A. Al-Mekhaizim

**Abstract:**

**Keywords:**
Multilevel converters,
harmonics,
pulse widthmodulation (PWM),
optimal control.

##### 2187 Selection of Designs in Ordinal Regression Models under Linear Predictor Misspecification

**Authors:**
Ishapathik Das

**Abstract:**

**Keywords:**
Model misspecification,
multivariate kriging,
multivariate logistic link,
ordinal response models,
quantile
dispersion graphs.

##### 2186 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.

##### 2185 Detecting Earnings Management via Statistical and Neural Network Techniques

**Authors:**
Mohammad Namazi,
Mohammad Sadeghzadeh Maharluie

**Abstract:**

**Keywords:**
Earnings management,
generalized regression neural
networks,
linear regression,
multi-layer perceptron,
Tehran stock
exchange.

##### 2184 Predicting Bridge Pier Scour Depth with SVM

**Authors:**
Arun Goel

**Abstract:**

**Keywords:**
Modeling,
pier scour,
regression,
prediction,
SVM
(Poly & Rbf kernels).

##### 2183 Modeling Aeration of Sharp Crested Weirs by Using Support Vector Machines

**Authors:**
Arun Goel

**Abstract:**

**Keywords:**
Air entrainment rate,
dissolved oxygen,
regression,
SVM,
weir.

##### 2182 A Study on a Research and Development Cost-Estimation Model in Korea

**Authors:**
Babakina Alexandra,
Yong Soo Kim

**Abstract:**

In this study, we analyzed the factors that affect research funds using linear regression analysis to increase the effectiveness of investments in national research projects. We collected 7,916 items of data on research projects that were in the process of being finished or were completed between 2010 and 2011. Data pre-processing and visualization were performed to derive statistically significant results. We identified factors that affected funding using analysis of fit distributions and estimated increasing or decreasing tendencies based on these factors.

**Keywords:**
R&D funding,
Cost estimation,
Linear regression,
Preliminary feasibility study.

##### 2181 Liquid Chromatography Microfluidics for Detection and Quantification of Urine Albumin Using Linear Regression Method

**Authors:**
Patricia B. Cruz,
Catrina Jean G. Valenzuela,
Analyn N. Yumang

**Abstract:**

Nearly a hundred per million of the Filipino population is diagnosed with Chronic Kidney Disease (CKD). The early stage of CKD has no symptoms and can only be discovered once the patient undergoes urinalysis. Over the years, different methods were discovered and used for the quantification of the urinary albumin such as the immunochemical assays where most of these methods require large machinery that has a high cost in maintenance and resources, and a dipstick test which is yet to be proven and is still debated as a reliable method in detecting early stages of microalbuminuria. This research study involves the use of the liquid chromatography concept in microfluidic instruments with biosensor as a means of separation and detection respectively, and linear regression to quantify human urinary albumin. The researchers’ main objective was to create a miniature system that quantifies and detect patients’ urinary albumin while reducing the amount of volume used per five test samples. For this study, 30 urine samples of unknown albumin concentrations were tested using VITROS Analyzer and the microfluidic system for comparison. Based on the data shared by both methods, the actual vs. predicted regression were able to create a positive linear relationship with an R^{2} of 0.9995 and a linear equation of y = 1.09x + 0.07, indicating that the predicted values and actual values are approximately equal. Furthermore, the microfluidic instrument uses 75% less in total volume – sample and reagents combined, compared to the VITROS Analyzer per five test samples.

**Keywords:**
Chronic kidney disease,
microfluidics,
linear regression,
VITROS analyzer,
urinary albumin.

##### 2180 Extended Least Squares LS–SVM

**Authors:**
József Valyon,
Gábor Horváth

**Abstract:**

**Keywords:**
Function estimation,
Least–Squares Support VectorMachines,
Regression,
System Modeling

##### 2179 The Profit Trend of Cosmetics Products Using Bootstrap Edgeworth Approximation

**Authors:**
Edlira Donefski,
Lorenc Ekonomi,
Tina Donefski

**Abstract:**

**Keywords:**
Bootstrap,
Edgeworth approximation,
independent and Identical distributed,
quantile.

##### 2178 Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas

**Authors:**
M. Moeinaddini,
Z. Asadi-Shekari,
M. Zaly Shah,
A. Hamzah

**Abstract:**

**Keywords:**
Green travel modes,
urban travel indicators,
daily
trips by public transport,
multi-linear regression analysis.

##### 2177 Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs

**Authors:**
Surinder Deswal,
Mahesh Pal

**Abstract:**

**Keywords:**
Artificial neural network,
evaporation losses,
multiple linear regression,
modeling.

##### 2176 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.

##### 2175 Improvement of MLLR Speaker Adaptation Using a Novel Method

**Authors:**
Ing-Jr Ding

**Abstract:**

**Keywords:**
hidden Markov model,
maximum likelihood linearregression,
speech recognition,
speaker adaptation.