On the ML Estimation of the Correlation Parameter in Generalized Exponential Models

2011 ◽  
Author(s):  
Helmut Waldl ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras ◽  
Zacharias Anastassi
Keyword(s):  
Ml Estimation ◽  
Correlation Parameter ◽  
Exponential Models

scholarly journals AN EM ALGORITHM FOR FITTING A NEW CLASS OF MIXED EXPONENTIAL REGRESSION MODELS WITH VARYING DISPERSION

2020 ◽  
Vol 50 (2) ◽  
pp. 555-583 ◽  
Author(s):  
George Tzougas ◽  
Dimitris Karlis
Keyword(s):  
Regression Models ◽  
Real Data ◽  
Dispersion Parameters ◽  
Ml Estimation ◽  
Exponential Models ◽  
Exponential Regression ◽  
Natural Choice ◽  
Type Algorithm ◽  
Varying Dispersion ◽  
Heavy Tailed

AbstractRegression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM)-type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM-type algorithm.

Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan
Keyword(s):  
State Governments ◽  
The Novel ◽  
Curve Estimation ◽  
Exponential Models ◽  
Inverse Models ◽  
Forward Prediction ◽  
S Curve ◽  
Novel Coronavirus ◽  
Best Fit ◽  
Official Data

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

Standard Asymptotic Theory

2017 ◽  
Author(s):  
Russell Cheng
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Variance ◽  
Statistical Significance ◽  
Regularity Conditions ◽  
Parameter Estimates ◽  
Asymptotic Results ◽  
Ml Estimation ◽  
True Parameter ◽  
Log Likelihood ◽  
Ml Estimator

This book relies on maximum likelihood (ML) estimation of parameters. Asymptotic theory assumes regularity conditions hold when the ML estimator is consistent. Typically an additional third derivative condition is assumed to ensure that the ML estimator is also asymptotically normally distributed. Standard asymptotic results that then hold are summarized in this chapter; for example, the asymptotic variance of the ML estimator is then given by the Fisher information formula, and the log-likelihood ratio, the Wald and the score statistics for testing the statistical significance of parameter estimates are all asymptotically equivalent. Also, the useful profile log-likelihood then behaves exactly as a standard log-likelihood only in a parameter space of just one dimension. Further, the model can be reparametrized to make it locally orthogonal in the neighbourhood of the true parameter value. The large exponential family of models is briefly reviewed where a unified set of regular conditions can be obtained.

A class of dynamic piecewise exponential models with random time grid

2012 ◽  
Vol 142 (3) ◽  
pp. 728-742 ◽  
Author(s):  
Fabio N. Demarqui ◽  
Rosangela H. Loschi ◽  
Dipak K. Dey ◽  
Enrico A. Colosimo
Keyword(s):  
Random Time ◽  
Exponential Models ◽  
Piecewise Exponential ◽  
Time Grid

scholarly journals Wormhole Solutions in Symmetric Teleparallel Gravity with Noncommutative Geometry

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1260
Author(s):  
Zinnat Hassan ◽  
Ghulam Mustafa ◽  
Pradyumn Kumar Sahoo
Keyword(s):  
Linear Model ◽  
Noncommutative Geometry ◽  
Teleparallel Gravity ◽  
Exponential Model ◽  
Model Parameter ◽  
Exponential Models ◽  
Feasible Solutions ◽  
The Stability ◽  
Tov Equation

This article describes the study of wormhole solutions in f(Q) gravity with noncommutative geometry. Here, we considered two different f(Q) models—a linear model f(Q)=αQ and an exponential model f(Q)=Q−α1−e−Q, where Q is the non-metricity and α is the model parameter. In addition, we discussed the existence of wormhole solutions with the help of the Gaussian and Lorentzian distributions of these linear and exponential models. We investigated the feasible solutions and graphically analyzed the different properties of these models by taking appropriate values for the parameter. Moreover, we used the Tolman–Oppenheimer–Volkov (TOV) equation to check the stability of the wormhole solutions that we obtained. Hence, we found that the wormhole solutions obtained with our models are physically capable and stable.

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