scholarly journals A New Hyperprior Distribution for Bayesian Regression Model with Application in Genomics

2017 ◽  
Author(s):  
Renato Rodrigues Silva
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Coefficient ◽  
R Package ◽  
Bayesian Regression ◽  
Predictor Variables ◽  
Chi Square ◽  
Wide Context ◽  
Genome Wide ◽  
Large P Small N

AbstractIn the regression analysis, there are situations where the model have more predictor variables than observations of dependent variable, resulting in the problem known as “large p small n”. In the last fifteen years, this problem has been received a lot of attention, specially in the genome-wide context. Here we purposed the bayes H model, a bayesian regression model using mixture of two scaled inverse chi square as hyperprior distribution of variance for each regression coefficient. This model is implemented in the R package BayesH.

scholarly journals Simulation Study for Penalized Bayesian Elastic Net Quantile Regression

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Muntadher Almusaedi ◽  
Ahmad Naeem Flaih
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Regression Model ◽  
Prior Distribution ◽  
Main Idea ◽  
Bayesian Regression ◽  
Elastic Net ◽  
Adaptive Lasso ◽  
Quantile Regression Model ◽  
Truncated Gamma Distribution

Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the Bayesian regression model, the variance of the estimators are reduced notable and the bias is getting smaller. The tradeoff between the bias and variance of the penalized Bayesian regression estimator consequently produce more interpretable model with more prediction accuracy. In this paper, we proposed new hierarchical model for the Bayesian quantile regression by employing the scale mixture of normals mixing with truncated gamma distribution that stated by (Li and Lin, 2010) as Laplace prior distribution. Therefore, new Gibbs sampling algorithms are introduced. A comparison has made with classical quantile regression model and with lasso quantile regression model by conducting simulations studies. Our model is comparable and gives better results.

Improved weather indices based Bayesian regression model for forecasting crop yield

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Crop Yield ◽  
Wheat Yield ◽  
Bayesian Framework ◽  
Bayesian Regression ◽  
Estimation Of Parameters ◽  
Yield Data ◽  
Conjugate Priors ◽  
Modern Era

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.

scholarly journals PEMODELAN ANGKA KEMATIAN KECELAKAAN LALU LINTAS DI KOTA DENPASAR

2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
NI LUH WIWIN YUNIARTI ◽  
I GUSTI AYU MADE SRINADI ◽  
MADE SUSILAWATI
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Traffic Congestion ◽  
Poisson Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Poisson Regression Model ◽  
Chi Square ◽  
Poisson Regression Analysis ◽  
Accident Data

Denpasar City is one of the most crowded areas on the island of Bali, this is due to the fast population growth rate. This fast population can cause problems, one of the problem is in the transportation sector. The increase in the volume of transportation can cause traffic congestion which can lead to a high number of traffic accidents, this can lead to death due to traffic accidents in Denpasar City. To determine the factors that influence traffic accident mortality, researchers used Poisson regression analysis. Based on data on traffic accidents in Denpasar City in 2018, the deviance value is smaller than the chi square value. Therefore Poisson regression analysis is sufficient to model traffic accident data in Denpasar City. The Poisson regression model obtained from this research is. Based on the Poisson regression model obtained, the independent variable that contributes significantly and has a high effect on the number of people who die in traffic accidents is the driver factor.

scholarly journals Mixed estimator of spline truncated, Fourier series, and kernel in biresponse semiparametric regression model

2021 ◽  
Vol 880 (1) ◽  
pp. 012046
Author(s):  
Hartina Husain ◽  
I N Budiantara ◽  
Ismaini Zain
Keyword(s):  
Regression Analysis ◽  
Fourier Series ◽  
Regression Model ◽  
Semiparametric Regression ◽  
Least Square ◽  
Predictor Variables ◽  
Parameter Estimates ◽  
Semiparametric Regression Model ◽  
Estimation Techniques ◽  
Truncated Fourier Series

Abstract Regression analysis is a method of analysis to determine the relationship between the response and the predictor variables. There are three approaches in regression analysis, namely the parametric, nonparametric, and semiparametric approaches. Biresponse Semiparametric regression model is a regression model that uses a combination approach between parametric and nonparametric components, where two response variables are correlated with each other. For data cases with several predictor variables, different estimation technique approaches can be used for each variable. In this study, the parametric component is assumed to be linear. At the same time, the nonparametric part is approached using a mixture of three estimation techniques, namely, spline truncated, Fourier series, and the kernel. The unknown data pattern is assumed to follow the criteria of each of these estimation techniques. The spline is used when the data pattern tends to change at certain time intervals, the Fourier series is used when the data pattern tends to repeat itself, and the kernel is used when the data does not have a specific way. This study aims to obtain parameter estimates for the mixed semiparametric regression model of spline truncated, Fourier series, and the kernel on the biresponse data using the Weighted Least Square (WLS) method. The formed model depends on the selection of knot points, oscillation parameters, and optimal bandwidth. The best model is based on the smallest Generalized Cross Validation (GCV).

scholarly journals Improved weather indices based Bayesian regression model for forecasting crop yield

MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Crop Yield ◽  
Wheat Yield ◽  
Bayesian Framework ◽  
Bayesian Regression ◽  
Estimation Of Parameters ◽  
Yield Data ◽  
Conjugate Priors ◽  
Modern Era

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.

Pengaruh Motivasi, Self Awareness Dan Komunikasi terhadap Disiplin Kerja Karyawan di Perusahaan Daerah Air Minum (Pdam) Kabupaten Ponorogo

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Fitriana Fitriana ◽  
Umi Farida ◽  
Tegoeh Hari Abrianto
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Water Supply ◽  
Regression Coefficient ◽  
Reliability Test ◽  
Self Awareness ◽  
Validity And Reliability ◽  
Communication Variable ◽  
Water Supply Company ◽  
Regional Water

This study aims to determine the effect of motivation, self awareness and communication on the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency. The location of research in Pramuka Street Number 21, Nologaten, Ponorogo Regency. The population in this study was 102 employees. The sample in this study used 50 respondents. Data collection techniques using questionnaires, then tested with validity and reliability test, while the method of data analysis using multiple regression analysis with the help of SPSS and hypothesis testing partially or simultaneously. The results showed that; (1) Motivation partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with a regression coefficient of 0.317, t value of 2.903> t table of 2.012 and sig. of 0.006 <0.05, (2) Self Awareness partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the results of the regression coefficient of 0.409, t value of 3.478> t table of 2.012 and sig. of 0.001 <0.05, (3) Communication partially influences the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the results of a regression coefficient of 0.310, t value of 2.178> t table of 2.012 and sig. of 0.035 <0.05, (4) Motivation, self awareness and communication simultaneously affect employee work discipline in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the calculated F value of 14.807> F table 2.81 and sig value. of 0,000 <0.05, (5) Self awareness is the most dominant variable affecting the work discipline of employees in the Regional Water Supply Company (PDAM) of Ponorogo Regency with the result of self awareness variable t value of 3.478 is greater than the value of t variable count motivation and communication variables. Furthermore, from the value of sig. the variable self awareness of 0.001 is smaller than the value of sig. motivation variable and communication variable.

Analisa Pemilihan Moda Transportasi Umum Rute Tanjung Karang – Bandara Radin Inten II Dengan Stated Preference dan Uji Crame’s V

2018 ◽  
Vol 2 (2) ◽  
pp. 137
Author(s):  
Muhammad Abi Berkah Nadi
Keyword(s):  
Regression Analysis ◽  
Utility Function ◽  
Stated Preference ◽  
Technical Analysis ◽  
Chi Square ◽  
Time Headway ◽  
Independent Variable ◽  
The Relationship ◽  
Selection Of

Radin Inten II Airport is a national flight in Lampung Province. In this study using the technical analysis stated preference which is the approach by conveying the choice statement in the form of hypotheses to be assessed by the respondent. By using these techniques the researcher can fully control the hypothesized factors. To determine utility function for model forecasting in fulfilling request of traveler is used regression analysis with SPSS program. The analysis results obtained that the passengers of the dominant airport in the selection of modes of cost attributes than on other attributes. From the result of regression analysis, the influence of independent variable to the highest dependent variable is when the five attributes are used together with the R square value of 8.8%. The relationship between cost, time, headway, time acces and service with the selection of modes, the provision that states whether or not there is a decision. The significance of α = 0.05 with chi-square. And the result of Crame's V test average of 0.298 is around the middle, then the relationship is moderate enough.

LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model

2020 ◽  
Author(s):  
Spark C. Tseung ◽  
Andrei Badescu ◽  
Tsz Chai Fung ◽  
Xiaodong Sheldon Lin
Keyword(s):  
Regression Model ◽  
R Package ◽  
Mixture Of Experts ◽  
Loss Modelling
Sign in / Sign up

Export Citation Format

Share Document