**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**243

# Search results for: Hedonic regression

##### 243 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.

##### 242 A Research on Inference from Multiple Distance Variables in Hedonic Regression – Focus on Three Variables

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

**Abstract:**

In urban context, urban nodes such as amenity or hazard will certainly affect house price, while classic hedonic analysis will employ distance variables measured from each urban nodes. However, effects from distances to facilities on house prices generally do not represent the true price of the property. Distance variables measured on the same surface are suffering a problem called multicollinearity, which is usually presented as magnitude variance and mean value in regression, errors caused by instability. In this paper, we provided a theoretical framework to identify and gather the data with less bias, and also provided specific sampling method on locating the sample region to avoid the spatial multicollinerity problem in three distance variable’s case.

**Keywords:**
Hedonic regression,
urban node,
distance variables,
multicollinerity,
collinearity.

##### 241 Valuing Environmental Impact of Air Pollution in Moscow with Hedonic Prices

**Authors:**
V. Komarova

**Abstract:**

The main purpose of this research is the calculation of implicit prices of the environmental level of air quality in the city of Moscow on the basis of housing property prices. The database used contains records of approximately 20 thousand apartments and has been provided by a leading real estate agency operating in Russia. The explanatory variables include physical characteristics of the houses, environmental (industry emissions), neighbourhood sociodemographic and geographic data: GPS coordinates of each house. The hedonic regression results for ecological variables show «negative» prices while increasing the level of air contamination from such substances as carbon monoxide, nitrogen dioxide, sulphur dioxide, and particles (CO, NO2, SO2, TSP). The marginal willingness to pay for higher environmental quality is presented for linear and log-log models.

**Keywords:**
Air pollution,
environment,
hedonic prices,
real estate,
willingness to pay.

##### 240 Hedonic Motivations for Online Shopping

**Authors:**
Pui-Lai To,
E-Ping Sung

**Abstract:**

The purpose of this study is to investigate hedonic online shopping motivations. A qualitative analysis was conducted to explore the factors influencing online hedonic shopping motivations. The results of the study indicate that traditional hedonic values, consisting of social, role, self-gratification, learning trends, pleasure of bargaining, stimulation, diversion, status, and adventure, and dimensions of flow theory, consisting of control, curiosity, enjoyment, and telepresence, exist in the online shopping environment. Two hedonic motivations unique to Internet shopping, privacy and online shopping achievement, were found. It appears that the most important hedonic value to online shoppers is having the choice to interact or not interact with others while shopping on the Internet. This study serves as a basis for the future growth of Internet marketing.

**Keywords:**
Internet Shopping,
Shopping Motivation,
Hedonic Motivation.

##### 239 Internet Shopping: A Study Based On Hedonic Value and Flow Theory

**Authors:**
Pui-Lai To,
E-Ping Sung

**Abstract:**

**Keywords:**
Flow theory,
hedonic motivation,
internet shopping.

##### 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 A Game-Theoretic Approach to Hedonic Housing Prices

**Authors:**
Cielito F. Habito,
Michael O. Santos,
Andres G. Victorio

**Abstract:**

**Keywords:**
Housing demand,
hedonics and valuation,
residentialmarkets.

##### 233 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.

##### 232 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.

##### 231 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.

##### 230 A Robust LS-SVM Regression

**Authors:**
József Valyon,
Gábor Horváth

**Abstract:**

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

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

**Authors:**
Gints Jekabsons

**Abstract:**

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

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

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

**Abstract:**

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

##### 227 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.

##### 226 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.

##### 225 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.

##### 224 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.

##### 223 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.

##### 222 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.

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

**Authors:**
Shiwei Deng,
Yang Bao

**Abstract:**

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

##### 220 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.

##### 219 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.

##### 218 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.

##### 217 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.

##### 216 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.

##### 215 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.

##### 214 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.