scholarly journals SPATIAL RESOLUTION ENHANCEMENT OF OVERSAMPLED IMAGES USING REGRESSION DECOMPOSITION AND SYNTHESIS

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 71-77
Author(s):  
H.-W. Chen
Keyword(s):  
Regression Analysis ◽  
Statistical Model ◽  
Regression Model ◽  
Spatial Resolution ◽  
Regression Models ◽  
Resolution Enhancement ◽  
Design Matrix ◽  
Statistical Inferences ◽  
Proposed Model ◽  
Regression Decomposition

Abstract. A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.

scholarly journals The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jin Xu ◽  
Chao Yi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Effective Theory ◽  
Standard Errors ◽  
Limited Data ◽  
Analysis Model ◽  
Multiple Objects ◽  
Model Categories ◽  
Model Expression

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

scholarly journals Bimodal Regression Model

2017 ◽  
Vol 40 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Guillermo Domingo Martinez ◽  
Heleno Bolfarine ◽  
Hugo Salinas
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Error Term ◽  
Bimodal Distribution ◽  
New Model ◽  
Special Cases ◽  
Error Distributions ◽  
New Distribution ◽  
Skew Normal

Regression analysis is a technique widely used in different areas ofhuman knowledge, with distinct distributions for the error term. Itis the case, however, that regression models with bimodal responsesor, equivalently, with the error term following a bimodal distribution are notcommon in the literature, perhaps due to the lack of simple to dealwith bimodal error distributions. In this paper we propose a simpleto deal with bimodal regression model with a symmetric-asymmetricdistribution for the error term for which for some values of theshape parameter it can be bimodal. This new distribution containsthe normal and skew-normal as special cases. A realdata application reveals that the new model can be extremely usefulin such situations.

Regression Analysis

2018 ◽  
pp. 170-205
Author(s):  
Quan Li
Keyword(s):  
Economic Growth ◽  
Regression Analysis ◽  
Statistical Model ◽  
Regression Model ◽  
Ordinary Least Squares ◽  
Theory And Practice ◽  
Model Fit ◽  
Theoretical Argument ◽  
Markov Theorem ◽  
The Relationship

This chapter teaches how to use R to conduct regression analysis to answer the question: Does trade promote economic growth? It demonstrates how to specify a statistical model from a theoretical argument, prepare data, estimate and interpret the statistical model, and use the estimated results to make inferences and answer the question of interest. More specifically, it discusses the logic of regression analysis, the relationship between population and sample regression models, how to estimate a regression model in theory and practice, the estimation of sample regression model using OLS (ordinary least squares), the interpretation of estimation results, the statistical inference in regression analysis using hypothesis testing and confidence interval, the types of sum of squares and overall model fit, and how to report the model results. The validity of regression analysis is contingent upon the assumptions of the Gauss-Markov theorem being met.

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Shin Zhu Sim ◽  
Ramesh C. Gupta ◽  
Seng Huat Ong
Keyword(s):  
Regression Model ◽  
Poisson Distribution ◽  
Likelihood Ratio ◽  
Regression Models ◽  
Discrete Data ◽  
Likelihood Ratio Tests ◽  
Simulation Studies ◽  
Generalized Poisson ◽  
Proposed Model ◽  
Better Than

Abstract In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

scholarly journals A New Composite Lognormal-Pareto Type II Regression Model to Analyze Household Budget Data via Particle Swarm Optimization

2021 ◽  
Author(s):  
Hande Konşuk Ünlü
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Regression Models ◽  
Particle Swarm ◽  
Model Parameters ◽  
Type Ii ◽  
Swarm Optimization ◽  
Household Budget Survey ◽  
Proposed Model ◽  
Household Budget

Abstract When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (2018)'s paper. In this study, a novel Lognormal-Pareto Type II composite regression model is proposed. Particle swarm optimization ( PSO ) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute ( TurkStat ). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO . Additionally, the proposed regression model performance is compared with Lognormal , Lomax , Gamma and Gamma-Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data.

scholarly journals Türkiye’de Organik Bal Üretiminin Yıllara Göre Değişiminin Regresyon Analizi ile İncelenmesi Üzerine Bir Çalışma

2018 ◽  
Vol 6 (11) ◽  
pp. 1507 ◽  
Author(s):  
Şenol Çelik ◽  
Turgay Şengül ◽  
Bünyamin Söğüt ◽  
A. Yusuf Şengül
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Mean Squared Error ◽  
Parameter Estimates ◽  
Suitable Model ◽  
Inverse Regression ◽  
Linear Quadratic ◽  
Honey Production ◽  
Organic Honey

In this study, changes in organic honey production in Turkey between 2004 and 2016 were examined by regression analysis. In regression analysis, linear, quadratic, cubic, logarithmic and inverse regression models have been studied comparatively. The R2 values obtained with these models are; 0.155, 0.616, 0.699, 0.366, 0.522, R ̅^2 values were found as 0.079, 0.539, 0.599, 0.308, 0.479 and MSE (Mean Squared Error) values were 48743.013, 24376.605, 21228.605, 36580.476, 27563.473, respectively. The quadratic regression model, in which the parameter estimates are significant, R ̅^2 is the highest and MSE is the lowest, is the most appropriate model. According to this regression model, estimated organic honey production yields in 2017 and 2018 are going to be 693 and 891 tons, respectively. In addition, regression analysis of non-organic honey production was done in the same period and linear regression model was determined as the most suitable model. For this model, R2= 0.772 and R ̅^2 = 0.750 were calculated. As a result, it has been concluded that organic and non-organic honey production yields can be estimated with different regression models.

THE DEVELOPMENT OF THE MATHEMATICAL MODEL OF PREDICTING THE INDICATORS OF ACCREDITATION OF TECHNICAL UNIVERSITIES IN THE RUSSIAN FEDERATION

2017 ◽  
pp. 27-38
Author(s):  
Olga Mikhaylovna Tikhonova ◽  
Alexander Fedorovich Rezchikov ◽  
Vladimir Andreevich Ivashchenko ◽  
Vadim Alekseevich Kushnikov
Keyword(s):  
Mathematical Model ◽  
System Dynamics ◽  
Regression Model ◽  
Oriented Graph ◽  
Real Data ◽  
Educational Process ◽  
Proposed Model ◽  
Linear Differential ◽  
The University ◽  
The Mathematical Model

The paper presents the system of predicting the indicators of accreditation of technical universities based on J. Forrester mechanism of system dynamics. According to analysis of cause-and-effect relationships between selected variables of the system (indicators of accreditation of the university) there was built the oriented graph. The complex of mathematical models developed to control the quality of training engineers in Russian higher educational institutions is based on this graph. The article presents an algorithm for constructing a model using one of the simulated variables as an example. The model is a system of non-linear differential equations, the modelling characteristics of the educational process being determined according to the solution of this system. The proposed algorithm for calculating these indicators is based on the system dynamics model and the regression model. The mathematical model is constructed on the basis of the model of system dynamics, which is further tested for compliance with real data using the regression model. The regression model is built on the available statistical data accumulated during the period of the university's work. The proposed approach is aimed at solving complex problems of managing the educational process in universities. The structure of the proposed model repeats the structure of cause-effect relationships in the system, and also provides the person responsible for managing quality control with the ability to quickly and adequately assess the performance of the system.

Regression model of assessment of customer solvency and banking risks in the process of lending

2019 ◽  
pp. 69-73
Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Credit Scoring ◽  
Bank Loans ◽  
Management Decisions ◽  
Computer Data ◽  
Proposed Model ◽  
Loan Risk

In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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