scholarly journals A ballooned beta-logistic model

2015 ◽  
Author(s):  
◽  
Min Yi
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Finite Interval ◽  
Extreme Values ◽  
Logistic Function ◽  
Enzyme Linked Immunosorbent Assay ◽  
Unknown Parameters ◽  
And Gender ◽  
Boundary Estimates ◽  
Flexible Model

The beta distribution is a simple and flexible model in which responses are naturally confined to the finite interval (0,1). Its parameters can be related to covariates such as dose and gender through a regression model. The Ballooned Beta-logistic (BBL) model expands the response boundaries from (0,1) to (L,U), where L and U are unknown parameters. Under the BBL model, expected responses follow a logistic function which can be made equal to that of the Four Parameter Logistic (4PL) model. But the distribution of responses differs from the classical 4PL model which has additive normal errors. In contrast, the BBL model naturally has bounded responses and inhomogeneous variance. The asymptotic normality of maximum likelihood estimators (MLEs) is obtained even though the support of this non-regular regression model depends on unknown parameters. We find MLEs converge faster to L and U than do extreme values at the minimum and maximum concentrations. Given enzyme-linked immunosorbent assay data from different plates, we study a motivating validation objective, which is to set suitability criteria for estimates of L and U; after this plates with boundary estimates outside these limits would be considered ”reference failures”. We show the BBL model has advantages over the 4PL model.

Missing Posterior Teeth and Risk of Temporomandibular Disorders

2009 ◽  
Vol 88 (10) ◽  
pp. 942-945 ◽  
Author(s):  
M.Q. Wang ◽  
F. Xue ◽  
J.J. He ◽  
J.H. Chen ◽  
C.S. Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Young Women ◽  
Temporomandibular Disorders ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Age And Gender ◽  
Posterior Teeth ◽  
Men 1 ◽  
And Gender

There is disagreement about the association between missing posterior teeth and the presence of temporomandibular disorders (TMD). Here, the purpose was to investigate whether the number of missing posterior teeth, their distribution, age, and gender are associated with TMD. Seven hundred and forty-one individuals, aged 21–60 years, with missing posterior teeth, 386 with and 355 without TMD, were included. Four variables—gender, age, the number of missing posterior teeth, and the number of dental quadrants with missing posterior teeth—were analyzed with a logistic regression model. All four variables—gender (OR = 1.59, men = 1, women = 2), age (OR = 0.98), the number of missing posterior teeth (OR = 0.51), and the number of dental quadrants with missing posterior teeth (OR = 7.71)—were entered into the logistic model (P < 0.01). The results indicate that individuals who lose posterior teeth, with fewer missing posterior teeth but in more quadrants, have a higher prevalence of TMD, especially young women.

scholarly journals Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1989
Author(s):  
Guillermo Martínez-Flórez ◽  
Hector W. Gomez ◽  
Roger Tovar-Falón
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Information Matrix ◽  
Link Function ◽  
Skew Normal Distribution ◽  
Linear Predictor ◽  
Response Variable ◽  
Proposed Model ◽  
Flexible Model ◽  
Skew Normal

Rate or proportion data are modeled by using a regression model. The considered regression model can be used for studying phenomena with a response on the (0, 1), [0, 1), (0, 1], or [0, 1] intervals. To connect the response variable with the linear predictor in the regression model, we use a logit link function, which guarantees that the obtained prediction ranges between zero and one in the cases inflated at zero or one (or both). The model is complemented with the assumption that the errors follow a power-skew-normal distribution, resulting in a very flexible model, and with a non-singular information matrix, constituting an advantage over other existing models in the literature. To explain the probability of point mass at the values zero and/or one (inflated part), we used a polytomic logistic model with covariates. The results of two illustrations showed that the proposed model is a better alternative compared to widely known models in the literature.

scholarly journals Socio-Emotional Variables Linked to the Consumption of Drugs amongst University Students of Social Sciences: A Pilot Study

2021 ◽  
Vol 18 (9) ◽  
pp. 4502
Author(s):  
José Luis Rodríguez-Sáez ◽  
Luis J. Martín-Antón ◽  
Alfonso Salgado-Ruiz ◽  
Miguel Ángel Carbonero
Keyword(s):  
Social Support ◽  
Emotional Intelligence ◽  
Academic Performance ◽  
University Students ◽  
Regression Model ◽  
Household Survey ◽  
Predictive Variables ◽  
Cannabis Consumption ◽  
And Gender ◽  
Mood Scale

This descriptive and transversal study, carried out on an intentional sample of 211 subjects who were split in terms of their consumption of psychoactive substances over the last month and who were aged between 18 and 28 (M = 21.36, and SD = 1.90), aimed to explore the emotional intelligence, perceived socio-family support and academic performance of university students vis-à-vis their consumption of drugs and to examine the link between them. The goal was to define university student consumer profile through a regression model using the multidimensional Perceived Social Support Scale (EMAS) and the Trait Meta Mood Scale-24 (TMMS-24) as instruments, together with academic performance and gender. The results report alcohol, tobacco, and cannabis consumption rates that are above the levels indicated by the Spanish household survey on alcohol and drugs in Spain (EDADES 2019) for the 15–34-year-old age range in Castilla y León. A certain link was observed between the consumption of substances and academic performance, although no differences were seen in academic performance in terms of consumer type. There was also no clear link observed between emotional intelligence and academic performance or between social support and academic performance. The predictive contribution of the variables included in the regression model was low (9%), which would advocate completing the model with other predictive variables until more appropriate predictability conditions can be found.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

scholarly journals Access to bank credit by smallholder farmers in Tanzania: a case study

Afrika Focus ◽  
2018 ◽  
Vol 31 (1) ◽  
Author(s):  
Nsubili Isaga
Keyword(s):  
Regression Model ◽  
Financial Services ◽  
Smallholder Farmers ◽  
Bank Credit ◽  
Survey Method ◽  
Cross Sectional ◽  
Study Objective ◽  
Factors Affecting ◽  
Logit Regression ◽  
And Gender

Tanzania like many other developing countries is highly dependent on agriculture for income generation and job creation for its citizens. Because the sector is mainly composed of smallholder farmers, lack of finance remains the leading obstacle to development. This study seeks to determine factors that affect access to bank credit by smallholder farmers in the Mvomero District of Morogoro, Tanzania. The study used a cross-sectional design, with data being collected via the survey method. Purposeful sampling was used to obtain the respondents who fitted into the study objective. The data were analyzed using descriptive statistics and a logistic regression model. The Logit regression model in particular was employed to determine factors that affect smallholder farmers’ access to bank services. The study reveals that the value of assets invested in farming activities, education and gender are significant factors affecting smallholder farmers’ access to bank credit. Policy recommendations include the establishment of a government bank that would exclusively provide financial services to agriculturalists by establishing a credit guarantee scheme, and the development of new financial products by the banks that would cater to the needs of smallholder farmers. Key words: access, bank credit, smallholder farmers, logit regression

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

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

scholarly journals COMPARATIVE ANALYSIS OF DIFFERENT CRITERIA OF SITE STRUCTURAL STABILITY IN THE TIME SERIES

2019 ◽  
pp. 119-128
Author(s):  
Liliya Andreevna Landman ◽  
Andrei Vladimirovich Faddeenkov
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Structural Changes ◽  
Least Squares Method ◽  
Complex Structure ◽  
Standard Normal Distribution ◽  
Regression Equations ◽  
Unknown Parameters ◽  
Chow Test ◽  
Wide Range

The concept of structure is used to describe a set of stable relations between the main parts of the object, which describe its integrity and identity, i.e, preserving the basic properties for a wide range of internal and external changes. This concept usually relates to the concepts of system and organization. The structure expresses a stable part of the system that is slightly changed during different reforms. Over the years structural changes take place because of active economic policy or as a result of spontaneous, uncontrollable processes. Therefore, it seems to be quite natural to find out whether there have been structural changes in the observation period, and to find them reflected in the specification of the model. The basic ideas of methods for determining structural changes in the time series dynamics have been considered, such as Chow test, Gujarati test and Poirier method. The power study was conducted for the three possible cases of change in time series trends. The random error was modeled according to the standard normal distribution. A linear multiple regression model with three independent variables was used as a time series model. Estimation of the vector of unknown parameters of the model was conducted using least squares method. For each of the three criteria the of test the null hypothesis about time series instability was carried out using the F -criterion, which involves finding the residual sum of squares of a regression model and analysis of correlation between its decline and the loss of degrees of freedom. It can be noted that Gujarati and Poirier equations have a more complex structure than equation of Chow test; however, using Chow test assumes estimation of the parameters of the three regression equations.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

scholarly journals Increased levels of VCAM-1 in sera and VLA-4 expression on neutrophils in dermatomyositis with interstitial lung disease

2020 ◽  
Author(s):  
Meiyi Lin ◽  
Xudong Liu ◽  
Chunshu Yang ◽  
Shan Zhao ◽  
Bailing Tian ◽  
...  
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Diffuse Alveolar Damage ◽  
Total Serum ◽  
Enzyme Linked Immunosorbent Assay ◽  
Healthy Controls ◽  
High Resolution Computed Tomography ◽  
Peripheral Blood Neutrophil ◽  
Late Antigen ◽  
And Gender

Abstract Background: Vascular cell adhesion molecule-1(VCAM-1) and its ligand very late antigen (VLA-4) play important roles in many autoimmune diseases. Our study aimed to investigate serum VCAM-1 level and VLA-4 expression on peripheral blood neutrophil surface in patients with dermatomyositis (DM), especially focusing on patients with interstitial lung disease(ILD). Methods: Blood specimens of 30 patients with DM and 30 healthy controls matched for age and gender were recruited. Total serum VCAM-1 level was measured using commercial enzyme-linked immunosorbent assay (ELISA) and the percentages of VLA-4 expression on the surface of neutrophils were analyzed by flow cytometry. We divided patients into subgroups according to whether they had ILD and whether they exhibited diffuse alveolar damage (DAD) via high-resolution computed tomography (HRCT).Results: Serum VCAM-1 levels were increased in DM patients compared with healthy controls (p<0.001). Patients with DM-ILD had higher serum VCAM-1 levels than those with none-ILD (p=0.015). The VCAM-1 levels were significantly increased in the DM-DAD group compared to the none-DAD group (p=0.002). The percentages of VLA-4 expression on neutrophils surface in DM patients were significantly elevated than that in healthy controls (p<0.001). The percentage of VLA-4 expression on neutrophils in DM patients with ILD were higher than none-ILD group(p=0.013). In the patients with ILD, DAD group had higher percentage of VLA-4 expression on neutrophils than none-DAD group (p=0.008).Conclusions: Our findings indicated that serum VCAM-1 level could be used as a potential serological biomarker for DM-ILD.

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