scholarly journals Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Guojun Gan
Keyword(s):  
Linear Regression ◽  
Life Insurance ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Models ◽  
Insurance Companies ◽  
Linear Regression Models ◽  
Insurance Product ◽  
Valuation Process ◽  
Speed Up

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

scholarly journals Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions

2018 ◽  
Author(s):  
Guojun Gan
Keyword(s):  
Linear Regression ◽  
Life Insurance ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Models ◽  
Insurance Companies ◽  
Linear Regression Models ◽  
Insurance Product ◽  
Valuation Process ◽  
Speed Up

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

scholarly journals Regression models for estimating chick hatchling weight from some egg geometry traits

2018 ◽  
Vol 34 (3) ◽  
pp. 323-334
Author(s):  
Nadya Mincheva ◽  
Mitko Lalev ◽  
Magdalena Oblakova ◽  
Pavlina Hristakieva
Keyword(s):  
Linear Regression ◽  
Surface Area ◽  
Regression Models ◽  
Linear Models ◽  
Geometric Mean ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Linear Regression Models ◽  
Egg Weight ◽  
Mean Diameter

The prediction of chicks? weight before hatching is an important element of selection, aimed at improving the uniformity rate and productivity of birds. With this regards, our goal was to develop and evaluate optimum models for similar prediction in two White Plymouth Rock chickens lines - line L and line K on the basis of the incubation egg weight and egg geometry characteristics - egg maximum breadth (B), egg length (L), geometric mean diameter (Dg), egg volume (V), egg surface area (S). A total of 280 eggs (140 from each line) laid by 40-weekold hens were randomly selected. Mean arithmetic values, standard deviations and coefficients of variation of studied parameters were determined for each line. Correlation coefficients between the weight of hatchlings and predictors were the highest for egg weight, geometric mean diameter, volume and surface area of eggs (r=0.731-0.779 for line L; r=0.802-0.819 for line ?). Nine linear regression models were developed and their accuracy evaluated. The regression equations of hatchlings? weight vs egg length had the lowest coefficient of determination (0.175 for line K and 0.291 for line L), but when egg length and breadth entered the model together, its value increased significantly up to 0.541 and 0.665 for lines L and K, respectively. The weight of day-old chicks from line L could be predicted with higher accuracy with a model involving egg surface area apart egg weight (ChW=0.513EW+0.282S - 10.345; R2=0.620). In line ? a more accurate prognosis was attained by adding egg breadth as an additional predictor to the weight in the model (ChW=0.587EW+0.566? - 19.853; R2=0.692). The study demonstrated that multiple linear regression models were more precise that single linear models.

scholarly journals Estimation of Causal Functional Linear Regression Models

2017 ◽  
Vol 9 (6) ◽  
pp. 106
Author(s):  
J.C.S. De Miranda
Keyword(s):  
Linear Regression ◽  
Tensor Product ◽  
Linear Regression Model ◽  
Regression Models ◽  
Linear Models ◽  
Linear Regression Models ◽  
Functional Responses ◽  
Functional Linear Models ◽  
Triangular Region ◽  
Functional Linear Regression

We present a methodology for estimating causal functional linear models using orthonormal tensor product expansions. More precisely, we estimate the functional parameters $\alpha$ and $\beta$ that appear in the causal functional linear regression model:$$\mathcal{Y}(s)=\alpha(s)+\int_a^b\beta(s,t)\mathcal{X}(t)\mathrm{d}t+\mathcal{E}(s),$$ where  $\mbox{supp } \beta \subset \mathfrak{T},$ and $\mathfrak{T}$ is the closed triangular region whose vertexes are $(a,a) , (b,a)$ and $(b,b).$ We assume we have an independent sample $\{ (\mathcal{Y}_k,\mathcal{X}_k) : 1\le k \le N, k\in \mathbb{N}\}$ of observations where the $\mathcal{X}_k $'s are functional covariates, the $\mathcal{Y}_k$'s are time order preserving functional responses and $\mathcal{E}_k,$ $1\le k \le N,$ is i.i.d. zero mean functional noise.

Development and Evaluation of Regression Models for the Prediction of Human Torque Strength Applied in the Prone Position

1989 ◽  
Vol 33 (11) ◽  
pp. 703-707
Author(s):  
Thomas Weaver ◽  
Subramanian Deivanayagam
Keyword(s):  
Experimental Investigation ◽  
Linear Regression ◽  
Prone Position ◽  
Predictive Models ◽  
Regression Models ◽  
Linear Regression Models ◽  
Point Location ◽  
Strength Data ◽  
Torque Values

This paper describes an experimental investigation conducted to develop and test predictive models for human torque strength in simulated maintenance tasks performed in the prone position. Eight linear regression models were developed for the prediction of human torque strength applied with a socket wrench while lying in the prone position. The models utilized anthropometric and strength data from 40 subjects in conjunction with task point location variables in their formulation. The predictability of each model was evaluated by comparing the observed and predicted torque values from 10 additional subjects.

scholarly journals METHODS OF THE ESTIMATED TWO-PARAMETER LINEAR REGRESSION MODELSFOR DIAGNOSIS OF BRONCHIAL ASTHMA SEVERITY IN CHILDREN

2021 ◽  
pp. 23-30
Author(s):  
PIHNASTYI OLEH MYKHAILOVYCH ◽  
KOZHYNA OLGA SERGEYEVNA
Keyword(s):  
Linear Regression ◽  
Bronchial Asthma ◽  
Regression Models ◽  
Linear Models ◽  
Asthma Severity ◽  
Linear Regression Models ◽  
Experimental Data Processing ◽  
Preliminary Experimental Data ◽  
Factor Regression ◽  
Two Parameter

Objectives: Prognostication of bronchial asthma severity in children by means of two-parameter regression models building. Methods: A clinical study of 70 children with bronchial asthma diagnosis of 6 to 18 years old was done.142 factors were analyzed and a degree of relationship among them was revealed. Single-factor regression models were used during preliminary experimental data processing. Results: The correlation connection between the value observed and the factors under research was revealed. The method of two-parameter linear models with a fair accuracy was developed. Conclusion: The suggested method of approximate two-parameter linear regression models can be used for preliminary analysis of medical research data where the value observed depends on a big number of loosely connected factors.

scholarly journals Inflation and Economic Growth in Iran: Evidence from ARDL & Rolling Linear Models

2011 ◽  
Vol 3 (6) ◽  
pp. 383-388
Author(s):  
Yazdan Naghdi ◽  
Mohadese Soltantooye .
Keyword(s):  
Economic Growth ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Models ◽  
Growth Models ◽  
Influential Factors ◽  
Linear Regression Models ◽  
Adjusted Model ◽  
The Relationship

Economists pay considerable attention to the influential factors on the economic growth in the framework of the growth models. In the same direction, the relationship between inflation and economic growth in Iran has been investigated during 1978-2008. First, an adjusted model has been designed based on (Barro) model and then the relationship between inflation and economic growth has been estimated using both ARDL and rolling linear regression models. The results derived from the both estimated models showed that the effect of inflation on economic growth is negative and Significance.

scholarly journals Study of the dependencies between more important pomological indicators in Cornel-Tree genotypes

2019 ◽  
Vol 42.1 ◽  
pp. 7153-7161
Author(s):  
Argir Zhivondov ◽  
Neli Keranova ◽  
Svetla Pandova
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Regression Models ◽  
Linear Models ◽  
Fruit Weight ◽  
Linear Regression Models ◽  
Complex Effect ◽  
Cornus Mas ◽  
The Subject ◽  
Positive Effect

The object of this study is nine genotypes of Cornus mas L.: Kazanlashki pear-shaped, Pancharevski cylindrical, Shumenski oblong, Yaltenski, Vratsa-Castel Sandryan, Atkov cornel-tree, Tsarigradski yellow and Yellow Hadjiiski, distributed in the territory of Bulgaria. The objective of the study is the analysis of the impacts between more important pomological indicators and their presentation through linear models. The impacts between weight, length and width of the fruit, length of the stem, weight, length and width of the stone more important pomological indicators were researched by applying correlation analysis. The proven dependencies were evaluated and modelled by linear regression models presenting the complex effect of the tested signs on the weight of the fruit. The length of the fruit (0.907), its width (0.746), and the length of the stem (0.605), the stone weight (0.755), its length (0.787) and its width (0.605) had positive effect on fruit weight. After a regression equation was worked out, it was found that 90% of the dispersion of the dependent variable could be explained by the alteration of the irrigation, soil cultivation, pruning, which are not the subject of this study.

scholarly journals Model-based Informal Inference

2017 ◽  
Vol 6 (5) ◽  
pp. 140
Author(s):  
Theodosia Prodromou
Keyword(s):  
Linear Regression ◽  
Instructional Practices ◽  
Linear Model ◽  
Regression Models ◽  
Linear Models ◽  
Linear Regression Models ◽  
Journal Paper ◽  
Support Students ◽  
Best Fit ◽  
Informal Inference

Following recent scholarly interest in teaching informal linear regression models, this study looks at teachers’ reasoning about informal lines of best fit and their role in pedagogy. The case results presented in this journal paper provide insights into the reasoning used when developing a simple informal linear model to best fit the available data. This study also suggests potential in specific aspects of bidirectional modelling to help foster the development of robust knowledge of the logic of inference for those investigating and coordinating relations between models developed during modelling exercises and informal inferences based on these models. These insights can inform refinement of instructional practices using simple linear models to support students’ learning of statistical inference, both formal and informal.

scholarly journals empirical likelihood ratio based comparative study on tests for normality of residuals in linear models

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Chioneso Marange ◽  
Yongsong Qin
Keyword(s):  
Monte Carlo ◽  
Linear Regression ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Regression Models ◽  
Linear Models ◽  
Type I Error ◽  
Type I ◽  
Simple Linear Regression ◽  
Linear Regression Models

The application of goodness-of-fit (GoF) tests in linear regression modeling is a common practice in applied statistical sciences. For instance, in simple linear regression the assumption of normality of residuals is always necessary to test before making any further inferences. The growing popularity of the use of powerful and efficient empirical likelihood ratio (ELR) based GoF tests in checking for departures from normality in various continuous distributions can be of great use in checking for distributional assumptions of residuals in linear models. Motivated by the attractive properties of the ELR based GoF tests the researchers conducted an extensive Type I error rate assessment as well as a Monte Carlo power comparison of selected ELR GoF tests with well-known existing tests against symmetric and asymmetric alternative OLS and BLUS residuals. Under the simulated scenarios, all the studied tests have good control of Type I error rates. The Monte Carlo experiments revealed the superiority of the ELR GoF tests under certain alternatives of both the OLS and BLUS residuals. Our findings also demonstrated the superiority of OLS over BLUS residuals when one is testing for normality in simple linear regression models. A real data study further revealed the applicability of the ELR based GoF tests in testing normality of residuals in linear regression models.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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