Direction Estimation in a General Regression Model with Discrete Predictors

2016 ◽  
pp. 77-88
Author(s):  
Yuexiao Dong ◽  
Zhou Yu
Keyword(s):  
Regression Model ◽  
Direction Estimation ◽  
General Regression

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Haar Wavelets in Data Analysis

2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian
Keyword(s):  
Data Analysis ◽  
Regression Model ◽  
Approximation Theory ◽  
Regression Models ◽  
Haar Wavelets ◽  
General Regression

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.

scholarly journals Credibility in the Regression case Revisited (A Late Tribute to Charles A. Hachemeister)

1997 ◽  
Vol 27 (1) ◽  
pp. 83-98 ◽  
Author(s):  
H. Bühlmann ◽  
A. Gisler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Regression Line ◽  
Good Choice ◽  
Design Matrix ◽  
Simple Linear Regression ◽  
One Dimensional ◽  
Regression Parameters ◽  
Definition Of ◽  
General Regression

AbstractMany authors have observed that Hachemeisters Regression Model for Credibility – if applied to simple linear regression – leads to unsatisfactory credibility matrices: they typically ‘mix up’ the regression parameters and in particular lead to regression lines that seem ‘out of range’ compared with both individual and collective regression lines. We propose to amend these shortcomings by an appropriate definition of the regression parameters:–intercept–slopeContrary to standard practice the intercept should however not be defined as the value at time zero but as the value of the regression line at the barycenter of time. With these definitions regression parameters which are uncorrected in the collective can be estimated separately by standard one dimensional credibility techniques.A similar convenient reparametrization can also be achieved in the general regression case. The good choice for the regression parameters is such as to turn the design matrix into an array with orthogonal columns.

A general regression model for statistical analysis of strain–life fatigue data

2008 ◽  
Vol 62 (21-22) ◽  
pp. 3639-3642 ◽  
Author(s):  
Enrique Castillo ◽  
Alfonso Fernández-Canteli ◽  
Hernán Pinto ◽  
Manuel López-Aenlle
Keyword(s):  
Statistical Analysis ◽  
Regression Model ◽  
Fatigue Data ◽  
General Regression

scholarly journals Characteristic Curve and Its Use in Determining the Compressive Strength of Concrete by the Rebound Hammer Test

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2705 ◽  
Author(s):  
Dalibor Kocáb ◽  
Petr Misák ◽  
Petr Cikrle
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Characteristic Curve ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Confidence Bands ◽  
Rebound Hammer ◽  
Practical Applications ◽  
General Regression

During the construction of concrete structures, it is often useful to know compressive strength at an early age. This is an amount of strength required for the safe removal of formwork, also known as stripping strength. It is certainly helpful to determine this strength non-destructively, i.e., without any invasive steps that would damage the structure. Second only to the ultrasonic pulse velocity test, the rebound hammer test is the most common NDT method currently used for this purpose. However, estimating compressive strength using general regression models can often yield inaccurate results. The experiment results show that the compressive strength of any concrete can be estimated using one’s own newly created regression model. A traditionally constructed regression model can predict the strength value with 50% reliability, or when two-sided confidence bands are used, with 95% reliability. However, civil engineers usually work with the so-called characteristic value defined as a 5% quantile. Therefore, it appears suitable to adjust conventional methods in order to achieve a regression model with 95% one-sided reliability. This paper describes a simple construction of such a characteristic curve. The results show that the characteristic curve created for the concrete in question could be a useful tool even outside of practical applications.

scholarly journals General Regression Model for Predicting Surface Topography after Abrasive Blasting

Metals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 938 ◽  
Author(s):  
Dagmar Draganovská ◽  
Gabriela Ižaríková ◽  
Anna Guzanová ◽  
Janette Brezinová
Keyword(s):  
Correlation Analysis ◽  
Regression Model ◽  
Surface State ◽  
Correlation Matrix ◽  
Regression Models ◽  
Roughness Parameters ◽  
Anova Analysis ◽  
Surface Irregularities ◽  
General Regression ◽  
Abrasive Blasting

Abrasive blasting modifies the surface state of pre-treated materials in terms of surface irregularities. Bearing in mind that the roughness characteristics affect the components functionality, it is essential to study and evaluation the surface state of pre-treated materials. The paper deals with evaluation of relation between individual parameters of roughness of the blasted surfaces by the correlation analysis. Based on the measured values on the surfaces which were blasted by various types of blasting devices, the correlation matrix was set and the standard of statistic importance of correlation between the monitored parameters was determined from it. The correlation coefficient was also set. There were found regression models using ANOVA (ANalysis Of Variance). Based on the analysis of the results were also proposed sets of roughness parameters, which can be used in the assessment of the blasted surfaces.

Minimum Bias Approximation of a General Regression Model

1973 ◽  
Vol 68 (343) ◽  
pp. 633-638 ◽  
Author(s):  
R. Cote ◽  
A. R. Manson ◽  
R. J. Hader
Keyword(s):  
Regression Model ◽  
Minimum Bias ◽  
General Regression

General regression model: A “model‐free” association test for quantitative traits allowing to test for the underlying genetic model

2019 ◽  
Vol 84 (3) ◽  
pp. 280-290
Author(s):  
Emilie Gloaguen ◽  
Marie‐Hélène Dizier ◽  
Mathilde Boissel ◽  
Ghislain Rocheleau ◽  
Mickaël Canouil ◽  
...  
Keyword(s):  
Regression Model ◽  
Quantitative Traits ◽  
Genetic Model ◽  
Free Association ◽  
Association Test ◽  
Model Free ◽  
General Regression
Sign in / Sign up

Export Citation Format

Share Document