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

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.

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Suppression-induced forgetting on a free-association test

Memory ◽

10.1080/09658211.2011.647036 ◽

2012 ◽

Vol 20 (2) ◽

pp. 100-109 ◽

Cited By ~ 12

Author(s):

Paula T. Hertel ◽

Daniel Large ◽

Ellen D. Stück ◽

Allison Levy

Keyword(s):

Free Association ◽

Association Test ◽

Induced Forgetting

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Real-Time Prediction of the COVID-19 Epidemic in Thailand using Simple Model-Free Method and Time Series Regression Model

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.10028 ◽

2021 ◽

Vol 18 (14) ◽

Author(s):

Rati WONGSATHAN

Keyword(s):

Genetic Algorithm ◽

Time Series ◽

Regression Model ◽

Regression Models ◽

Gaussian Function ◽

Logistic Function ◽

Hyperbolic Tangent ◽

Time Series Regression ◽

Model Free ◽

Tangent Function

The novel coronavirus 2019 (COVID-19) pandemic was declared a global health crisis. The real-time accurate and predictive model of the number of infected cases could help inform the government of providing medical assistance and public health decision-making. This work is to model the ongoing COVID-19 spread in Thailand during the 1st and 2nd phases of the pandemic using the simple but powerful method based on the model-free and time series regression models. By employing the curve fitting, the model-free method using the logistic function, hyperbolic tangent function, and Gaussian function was applied to predict the number of newly infected patients and accumulate the total number of cases, including peak and viral cessation (ending) date. Alternatively, with a significant time-lag of historical data input, the regression model predicts those parameters from 1-day-ahead to 1-month-ahead. To obtain optimal prediction models, the parameters of the model-free method are fine-tuned through the genetic algorithm, whereas the generalized least squares update the parameters of the regression model. Assuming the future trend continues to follow the past pattern, the expected total number of patients is approximately 2,689 - 3,000 cases. The estimated viral cessation dates are May 2, 2020 (using Gaussian function), May 4, 2020 (using a hyperbolic function), and June 5, 2020 (using a logistic function), whereas the peak time occurred on April 5, 2020. Moreover, the model-free method performs well for long-term prediction, whereas the regression model is suitable for short-term prediction. Furthermore, the performances of the regression models yield a highly accurate forecast with lower RMSE and higher R2 up to 1-week-ahead. HIGHLIGHTS COVID-19 model for Thailand during the first and second phases of the epidemic The model-free method using the logistic function, hyperbolic tangent function, and Gaussian function applied to predict the basic measures of the outbreak Regression model predicts those measures from one-day-ahead to one-month-ahead The parameters of the model-free method are fine-tuned through the genetic algorithm GRAPHICAL ABSTRACT

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Genetic Mapping of Quantitative Traits: Model-Free Sib-Pair Linkage Approaches

Statistical Advances in the Biomedical Sciences ◽

10.1002/9780470181218.ch27 ◽

2008 ◽

pp. 487-498

Author(s):

Saurabh Ghosh ◽

Partha P. Majumder

Keyword(s):

Genetic Mapping ◽

Quantitative Traits ◽

Model Free

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PSVIII-26 Gene editing of complex traits

Journal of Animal Science ◽

10.1093/jas/skz258.529 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 259-260

Author(s):

Ashley Ling ◽

Romdhane Rekaya

Keyword(s):

Genetic Engineering ◽

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Gene Editing ◽

Genetic Model ◽

Variable Number ◽

Production Parameters ◽

Potential Gain ◽

Better Than

Abstract Gene editing (GE) is a form of genetic engineering in which DNA is removed, inserted or replaced. For simple monogenic traits, the technology has been successfully implemented to create heritable modifications in animals and plants. The benefits of these niche applications are undeniable. For quantitative traits the benefits of GE are hard to quantify mainly because these traits are not genetic enough (low to moderate heritability) and their genetic architecture is often complex. Because its impact on the gene pool through the introduction of heritable modifications, the potential gain from GE must be evaluated within reasonable production parameters and in comparison, with available tools used in animal selection. A simulation was performed to compare GE with genomic selection (GS) and QTN-assisted selection (QAS) under four experimental factors: 1) heritability (0.1 or 0.4), 2) number of QTN affecting the trait (1000 or 10000) and their effect distribution (Gamma or uniform); 3) Percentage of selected females (100% or 33%); and 4) fixed or variable number of edited QTNs. Three models GS (M1), GS and GE (M2), and GS and QAS (M3) were implemented and compared. When the QTN effects were sampled from a Gamma distribution, all females were selected, and non-segregating QTNs were replaced, M2 clearly outperformed M1 and M3, with superiority ranging from 19 to 61%. Under the same scenario, M3 was 7 to 23% superior to M1. As the complexity of the genetic model increased (10000 QTN; uniform distribution), only one third of the females were selected, and the number of edited QTNs was fixed, the superiority of M2 was significantly reduced. In fact, M2 was only slightly better than M3 (2 to 6%). In all cases, M2 and M3 were better than M1. These results indicate that under realistic scenarios, GE for complex traits might have only limited advantages.

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