scholarly journals Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Survey Sliders and Visual Analog Scales

2020 ◽  
Author(s):  
Robert Kubinec
Keyword(s):  
Model Simulation ◽  
Human Subjects ◽  
R Package ◽  
Ordinary Least Squares ◽  
Beta Regression ◽  
Lower And Upper Bounds ◽  
College Professors ◽  
Visual Analog Scales ◽  
Proposed Model ◽  
Fitting Model

I propose a new model, ordered beta regression, for data collected from human subjects using slider scales/visual analog scales with lower and upper bounds. This model employs the cutpoint technique popularized by ordered logit to simultaneously estimate the probability that the outcome is at the upper bound, lower bound, or any continuous number in between. This model is contrasted with existing approaches, including ordinary least squares (OLS) regression and the zero-one-inflated beta regression (ZOIB) model. Simulation evidence shows that the proposed model, relative to existing approaches, estimates effects with more accuracy while capturing the full uncertainty in the distribution. Furthermore, an analysis of data on U.S. public opinion towards college professors reveals that the proposed model is better able to combine variation across continuous and degenerate responses. The model can be fit with the R package brms.

Modeling of Dynamic Loads on the Polished Rod of Pumping Unit in Case of Mulfunction of Rod Pump Operating

2014 ◽  
Vol 10 ◽  
pp. 95-101
Author(s):  
A.S. Topolnikov
Keyword(s):  
Multiphase Flow ◽  
Satisfactory Agreement ◽  
Model Simulation ◽  
Theoretical Modeling ◽  
Dynamic Loads ◽  
Plunger Pump ◽  
Proposed Model ◽  
Pumping Unit ◽  
Polished Rod

The paper presents the results of theoretical modeling of joined movement of pump rods and plunger pump and multiphase flow in a well for determination of dynamic loads on the polished rod of pumping unit. The specificity of the proposed model is the possibility of taking into account for complications in rod pump operating, such as leakage in valve steam, presence of gas and emulsion, incorrect fitting of plunger inside the cylinder pump. The satisfactory agreement of results of the model simulation with filed measurements are obtained.

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

2019 ◽  
Author(s):  
Leili Tapak ◽  
Omid Hamidi ◽  
Majid Sadeghifar ◽  
Hassan Doosti ◽  
Ghobad Moradi
Keyword(s):  
Regression Model ◽  
Simulation Study ◽  
Real Data ◽  
P Value ◽  
Parameter Estimates ◽  
Beta Regression ◽  
Rate Data ◽  
Data Set ◽  
Proposed Model ◽  
Beta Regression Model

Abstract Objectives Zero-inflated proportion or rate data nested in clusters due to the sampling structure can be found in many disciplines. Sometimes, the rate response may not be observed for some study units because of some limitations (false negative) like failure in recording data and the zeros are observed instead of the actual value of the rate/proportions (low incidence). In this study, we proposed a multilevel zero-inflated censored Beta regression model that can address zero-inflation rate data with low incidence.Methods We assumed that the random effects are independent and normally distributed. The performance of the proposed approach was evaluated by application on a three level real data set and a simulation study. We applied the proposed model to analyze brucellosis diagnosis rate data and investigate the effects of climatic and geographical position. For comparison, we also applied the standard zero-inflated censored Beta regression model that does not account for correlation.Results Results showed the proposed model performed better than zero-inflated censored Beta based on AIC criterion. Height (p-value <0.0001), temperature (p-value <0.0001) and precipitation (p-value = 0.0006) significantly affected brucellosis rates. While, precipitation in ZICBETA model was not statistically significant (p-value =0.385). Simulation study also showed that the estimations obtained by maximum likelihood approach had reasonable in terms of mean square error.Conclusions The results showed that the proposed method can capture the correlations in the real data set and yields accurate parameter estimates.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2021 ◽  
pp. 1203-1221
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Privacy Preservation ◽  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Global Network ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Efficient Manner ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

Bayesian beta regression with Bayesianbetareg R-package

2015 ◽  
Vol 31 (1) ◽  
pp. 165-187 ◽  
Author(s):  
Edilberto Cepeda-Cuervo ◽  
Daniel Jaimes ◽  
Margarita Marín ◽  
Javier Rojas
Keyword(s):  
R Package ◽  
Beta Regression

Kron–Branin modelling of ultra-short pulsed signal microelectrode

2018 ◽  
Vol 81 (2) ◽  
pp. 21001
Author(s):  
Zhifei Xu ◽  
Blaise Ravelo ◽  
Yang Liu ◽  
Lu Zhao ◽  
Fabien Delaroche ◽  
...  
Keyword(s):  
Model Simulation ◽  
Signal Propagation ◽  
Atom Probe ◽  
Physical Parameters ◽  
Characteristic Matrix ◽  
Nanosecond Duration ◽  
S Parameter ◽  
Proposed Model ◽  
Structure Problem ◽  
Short Signal

An uncommon circuit modelling of microelectrode for ultra-short signal propagation is developed. The proposed model is based on the Tensorial Analysis of Network (TAN) using the Kron–Branin (KB) formalism. The systemic graph topology equivalent to the considered structure problem is established by assuming as unknown variables the branch currents. The TAN mathematical solution is determined after the KB characteristic matrix identification. The TAN can integrate various structure physical parameters. As proof of concept, via hole ended microelectrodes implemented on Kapton substrate were designed, fabricated and tested. The 0.1-MHz-to-6-GHz S-parameter KB model, simulation and measurement are in good agreement. In addition, time-domain analyses with nanosecond duration pulse signals were carried out to predict the microelectrode signal integrity. The modelled microstrip electrode is usually integrated in the atom probe tomography. The proposed unfamiliar KB method is particularly beneficial with respect to the computation speed and adaptability to various structures.

Variable Precision Rough Set Model in Information Tables with Missing Values

2011 ◽  
Vol 15 (1) ◽  
pp. 110-116 ◽  
Author(s):  
Yoshifumi Kusunoki ◽  
◽  
Masahiro Inuiguchi
Keyword(s):  
Rough Set ◽  
Missing Values ◽  
Complete Information ◽  
Misclassification Error ◽  
Lower And Upper Bounds ◽  
Variable Precision ◽  
Similarity Relations ◽  
Proposed Model ◽  
Variable Precision Rough Set ◽  
Information Tables

In this paper, we study rough set models in information tables with missing values. The variable precision rough set model proposed by Ziarko tolerates misclassification error using a membership function in complete information tables. We generalize the variable precision rough set in information tables with missing values. Because of incompleteness, the membership degree of each objects becomes an interval value. We define six different approximate regions using the lower and upper bounds of membership functions. The properties of the proposed rough set model are investigated. Moreover we show that the proposed model is a generalization of rough set models based on similarity relations.

Hierarchical Optimization Model of Cloud Manufacturing Services Combination

2013 ◽  
Vol 464 ◽  
pp. 345-351 ◽  
Author(s):  
Han Zhang ◽  
Rui Feng Guo ◽  
Cong Geng
Keyword(s):  
Model Simulation ◽  
Cloud Manufacturing ◽  
Coarse Grained ◽  
Hierarchical Optimization ◽  
Combined Model ◽  
Implementation Processes ◽  
Proposed Model ◽  
Annealing Algorithm ◽  
Resource Combination

In this paper, a new mathematical combined model and the corresponding solution algorithm were proposed by analyzing the characteristics of services resource combination in cloud manufacturing. Aiming at avoiding problems of uncertainty, coarse-grained, diversity and dynamic in the process of services resource combination, a hierarchical model based on the hierarchical manufacturing implementation processes was firstly proposed. Then, quality of service (QoS) has been chosen to evaluate effects of services combination. Finally, a annealing algorithm was developed to solve the proposed model. Simulation experiment results prove the validity of the model and algorithm.

scholarly journals Regression Analysis of Masked Competing Risks Data under Cumulative Incidence Function Framework

2020 ◽  
Vol 49 (3) ◽  
pp. 25-29
Author(s):  
Yosra Yousif ◽  
Faiz Ahmed Mohamed Elfaki ◽  
Meftah Hrairi
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Model Simulation ◽  
Cumulative Incidence Function ◽  
Subdistribution Hazard ◽  
Incidence Function ◽  
Proposed Model ◽  
Competing Risks Data ◽  
Cause Of Failure ◽  
Proportional Subdistribution Hazard

In the studies that involve competing risks, somehow, masking issues might arise. That is, the cause of failure for some subjects is only known as a subset of possible causes. In this study, a Bayesian analysis is developed to assess the effect of risks factor on the Cumulative Incidence Function (CIF) by adopting the proportional subdistribution hazard model. Simulation is conducted to evaluate the performance of the proposed model and it shows that the model is feasible for the possible applications.

scholarly journals Median location problem with two probabilistic line barriers: Extending the Hook and Jeeves algorithm

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Saber Shiripour ◽  
Nezam Mahdavi-Amiri
Keyword(s):  
Location Problem ◽  
Programming Model ◽  
Distance Functions ◽  
Mixed Integer ◽  
Lower And Upper Bounds ◽  
Rectilinear Distance ◽  
Proposed Model ◽  
Integer Nonlinear Programming ◽  
Nonlinear Programming Model ◽  
New Facility

<p style='text-indent:20px;'>We consider a median location problem in the presence of two probabilistic line barriers on the plane under rectilinear distance. It is assumed that the two line barriers move on their corresponding horizontal routes uniformly. We first investigate different scenarios for the position of the line barriers on the plane and their corresponding routes, and then define the visibility and invisibility conditions along with their corresponding expected barrier distance functions. The proposed problem is formulated as a mixed-integer nonlinear programming model. Our aim is to locate a new facility on the plane so that the total weighted expected rectilinear barrier distance is minimized. We present efficient lower and upper bounds using the forbidden location problem for the proposed problem. To solve the proposed model, the Hooke and Jeeves algorithm (HJA) is extended. We investigate various sample problems to test the performance of the proposed algorithm and appropriateness of the bounds. Also, an empirical study in Kingston-upon-Thames, England, is conducted to illustrate the behavior and applicability of the proposed model.</p>

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