Assessment of Loss Ratio Model Performance: A Bayesian Approach

2017 ◽  
Author(s):  
Yang Liu
Keyword(s):  
Bayesian Approach ◽  
Model Performance ◽  
Loss Ratio ◽  
Ratio Model

scholarly journals Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach

2019 ◽  
Vol 51 (02) ◽  
pp. 249-266
Author(s):  
Nicholas D. Payne ◽  
Berna Karali ◽  
Jeffrey H. Dorfman
Keyword(s):  
Bayesian Approach ◽  
Model Uncertainty ◽  
Bayesian Model Averaging ◽  
Model Performance ◽  
Model Averaging ◽  
Forecasting Model ◽  
Forecast Errors ◽  
Forecast Horizon ◽  
Combining Forecasts ◽  
The Bayesian Approach

AbstractBasis forecasting is important for producers and consumers of agricultural commodities in their risk management decisions. However, the best performing forecasting model found in previous studies varies substantially. Given this inconsistency, we take a Bayesian approach, which addresses model uncertainty by combining forecasts from different models. Results show model performance differs by location and forecast horizon, but the forecast from the Bayesian approach often performs favorably. In some cases, however, the simple moving averages have lower forecast errors. Besides the nearby basis, we also examine basis in a specific month and find that regression-based models outperform others in longer horizons.

scholarly journals Identifying Risk Factors for Female Cardiovascular Disease Patients in Malaysia: A Bayesian Approach

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 15-23
Author(s):  
Nurliyana Juhan ◽  
Yong Zulina Zubairi ◽  
Zarina Mohd Khalid ◽  
Ahmad Syadi And Mahmood Zuhdi
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Renal Disease ◽  
Bayesian Approach ◽  
Model Performance ◽  
Killip Class ◽  
Female Patients ◽  
Increased Risk ◽  
Associated Risk Factors

Cardiovascular disease (CVD) includes coronary heart disease, cerebrovasculardisease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All femalesface the threat of CVD. But becoming aware of symptoms and signs is a great challengesince most adults at increased risk of cardiovascular disease (CVD) have no symptoms orobvious signs especially in females. The symptoms may be identified by the assessmentof their risk factors. The Bayesian approach is a specific way in dealing with this kindof problem by formalizing a priori beliefs and of combining them with the available ob-servations. This study aimed to identify associated risk factors in CVD among femalepatients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian lo-gistic regression and obtain a feasible model to describe the data. A total of 874 STEMIfemale patients in the National Cardiovascular Disease Database-Acute Coronary Syn-drome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov ChainMonte Carlo (MCMC) simulation approach was applied in the univariate and multivariateanalysis. Model performance was assessed through the model calibration and discrimina-tion. The final multivariate model of STEMI female patients consisted of six significantvariables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killipclass and age group. Females aged 65 years and above have higher incidence of CVD andmortality is high among female patients with Killip class IV. Also, renal disease was astrong predictor of CVD mortality. Besides, performance measures for the model wasconsidered good. Bayesian logistic regression model provided a better understanding onthe associated risk factors of CVD for female patients which may help tailor preventionor treatment plans more effectively.

scholarly journals Optimizing the performance of Kalman filter based statistical time-varying air quality models

2013 ◽  
Vol 12 (1) ◽  
pp. 27-39
Keyword(s):  
Kalman Filter ◽  
Bayesian Approach ◽  
Autoregressive Model ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Model Performance ◽  
Time Varying ◽  
Squared Prediction Error ◽  
Statistical Time ◽  
The Bayesian Approach

In this study, the Bayesian approach is proposed to estimate the noise variances of Kalman filter based statistical models for predicting the daily averaged PM10 concentrations of a typical coastal city, Macau, with Latitude 22°10’N and Longitude 113°34’E. By using the measurements in 2001 and 2002, the Bayesian approach is capable to estimate the most probable values of the noise variances in the Kalman filter based prediction models. It turns out that the estimated process noise variance of the time-varying autoregressive model with exogenous inputs, TVAREX, is significantly (~76%) less than that of the time-varying autoregressive model of order 1, TVAR(1), since the TVAREX model incorporates important mechanisms which govern the daily averaged PM10 concentrations in Macau. By further using data between 2003 and 2005, the choice of the noise variances is shown to affect the model performance, measured by the root-mean-squared error, of the TVAR(p) model and the TVAREX model. In addition, the optimal estimates of noise variances obtained by Bayesian approach for both models are located in the region where the model performance is insensitive to the choice of noise variances. Furthermore, the Bayesian approach will be demonstrated to provide more reasonable estimates of noise variances compared to the noise variances found by simply minimizing the root-mean-squared prediction error of the model. By comparing the optimized TVAREX model and the TVAR(p) models in predicting the daily averaged PM10 concentrations between 2003 and 2005, it is found that the TVAREX model outperforms the TVAR(p) models in terms of the general performance and the episode capturing capability.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

Sign in / Sign up

Export Citation Format

Share Document