1 edition of Estimating relationships in simulation models using regression found in the catalog.
Estimating relationships in simulation models using regression
Kenneth J. Euske
by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va
Written in English
This article demonstrates a procedure for using regression analysis to develop estimating equations that simply and quickly predict the effects on system performance of changes in parametric values of complex interrelation- ships imbedded in a full mathematical simulation model. Though a analytic solution to the relationships may be possible, the time and other resources necessary to generate such a solution may exceed those available. The resulting estimating equations facilitate understanding of the system being simulated, enable users to more easily conduct sensitivity analysis and answer what-if questions, and assist the selling of the simulation results to potential users. The procedure includes criteria for selection of: variables to be analyzed, the sensitivity range, the value increments, and the functional form. The example utilized is a simulation model developed for estimating future military retirement costs. (Author)
|Statement||by K.J. Euske, G.W. Thomas, and D.F. Smith|
|Contributions||Thomas, George W., Smith, Donald F., Naval Postgraduate School (U.S.). Dept. of Administrative Sciences|
|The Physical Object|
|Pagination||24 p. ;|
|Number of Pages||24|
•Ex. Simple linear regression Power Analysis Using Simulation standard errors and power for two-level models •Approximation for estimating standard errors for two-level models (Snijders & Bosker, ; Liu & Liang, ) •Accommodating CS, AR1, and TOEP residual covariance matricesFile Size: 2MB. Using Stata for Principles of Econometrics, Fourth Edition, by Lee C. Adkins and R. Carter Hill, is a companion to the introductory econometrics textbook Principles of Econometrics, Fourth Edition. Together, the two books provide a very good introduction to econometrics for undergraduate students and first-year graduate students.
Chapter 3 covers special topics in mediation analysis that are not normally found in books on mediation analysis. These include Monte Carlo simulation studies of mediation and moderated mediation, model misspecification due to omitted variables and confounders, instrumental variable estimation, sensitivity analysis, multiple group analysis of. Mohamed R. Abonazel: A Monte Carlo Simulation Study using R 6. The Application: Multiple linear regression model with autocorrelation problem In this application, we apply the above algorithm of Monte Carlo technic to compere between OLS and GLS estimators in multiple linear regression model when the errors are correlated withFile Size: 1MB.
Multivariable log-binomial and robust Poisson regression models were applied to estimate the risk ratio of having seven or more SABA canisters in each of the FeNO quartiles (using the lowest quartile as the reference group), controlling for age, gender, race/ethnicity, number of aeroallergen sensitivities, clinical center, FEV 1 % predicted Cited by: 9. Two of these are time series regression models and simulation models. Time series regression models. A time series regression model is used to estimate the trend followed by a variable over time, using regression techniques. A trend line shows the direction in which a variable is moving as time elapses.
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() The regression curve estimation by using mixed smoothing spline and kernel (MsS-K) model. Communications in Statistics - Theory and Meth () Nonparametric Estimation of Conditional Expectation With Auxiliary Information and Dimension by: Abstract.
This article demonstrates a procedure for using regression analysis to develop estimating equations that simply and quickly predict the effects on system performance of changes in parametric values of complex interrelation- ships imbedded in a full mathematical simulation model.
() Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion. Computational Statistics & Data Analysis. () Application of an imputation method for variance estimation under pseudo Cited by: Regression Analysis.
In statistics, regression analysis is a statistical technique for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables.
A metamodel is a simplification of the simulation model, representing the system's input–output relationship through a mathematical function with customized parameters. Regression analysis is one of the most useful and the most frequently used statistical methods [24, 3]. The aim of the regression methods is to describe the relationship between a response variable and one or more explanatory variables.
Among the different regression models, logistic regression plays a particular role. One remedy is to fit a generalized estimating equations (GEE) logistic regression model for the data, which is explored in this chapter. This chapter addresses repeated measures of the sampling unit, showing how the GEE method allows missing values within a subject without losing all the data from the subject, Author: Jeffrey R.
Wilson, Kent A. Lorenz. Estimating a Linear Regression. The R function for estimating a linear regression model is lm(y~x, data) which, used just by itself does not show any output; It is useful to give the model a name, such as mod1, then show the results using summary(mod1).
Least squares estimation for the multiple linear regression model with two predictors. The plane corresponds to the fitted least squares relationship, and the lengths of the vertical lines correspond to the residuals.
The sum of squared values of the lengths of the vertical lines is minimized by the plane. R's lm() function, ordinary least squares estimation and one of its limitations; R's formula syntax (Remember: y ~ x) All aspects are discussed using a previously simulated dataset.
As opposed to field work we do know the data generating process and thus can evaluate easily how well a regression model works. Estimating the Coefficients of the Linear Regression Model.
In practice, the intercept \(\beta_0\) and slope \(\beta_1\) of the population regression line are unknown.
Therefore, we must employ data to estimate both unknown parameters. In the following, a real world example will be used to demonstrate how this is achieved. regression models with copula families. This is an extension of a recent approach by Czado et al.
() and De Leon and Wu () who only consider a Gauss copula based on work by Song (, ) and Song et al. In our general copula-based regression approach, the model parameters can be esti-mated e ciently using maximum-likelihood File Size: KB. Chapter 10 Simple Linear Regression.
Assume you have data that is given in the form of ordered pairs \((x_1, y_1),\ldots, (x_n, y_n)\).In this chapter, we study the model \(y_i = \beta_0 + \beta_1 x_i + \epsilon_i\), where \(\epsilon_i\) are iid normal random variables with mean 0.
We develop methods for estimating \(\beta_0\) and \(\beta_1\), we examine whether the model is. In the pro- posed approach, the mean and the dispersion are modelled jointly using a heteroscedastic regression model.
The estimation of probabilities is based on the predicted mean, the predicted. We use the covariates selected in Section and fit the joint regression model for each copula family.
For each pair of copula families, we perform a corresponding Vuong test. Table 4 displays the results. For each pair, we display the copula family that is selected on a α = parentheses, we display the value of the Vuong test by: Estimation. When we estimate the parameters from the model, we need to minimise the sum of squared \(\varepsilon_t\) values.
If we minimise the sum of squared \(\eta_t\) values instead (which is what would happen if we estimated the regression model ignoring the autocorrelations in the errors), then several problems arise.
The estimated coefficients. Estimating a Linear Regression; Prediction with the Linear Regression Model; Repeated Samples to Assess Regression Coefficients; Estimated Variances and Covariance of Regression Coefficients; Non-Linear Relationships; Using Indicator Variables in a Regression; Monte Carlo Simulation; 3 Interval Estimation and.
Modal Regression using Kernel Density Estimation: a Review Yen-Chi Chen Abstract We review recent advances in modal regression studies using kernel density estima-tion.
Modal regression is an alternative approach for investigating relationship between a response variable and its covariates. Speci cally, modal regression summarizes theCited by: 1. Example Car (slide 1 of 2) Objective: To use logarithms of variables in a multiple regression to estimate a multiplicative relationship for automobile sales as a function of price, income, and interest rate.
Solution: The data set contains annual data on domestic auto sales in the United States. A multivariate regression analysis was conducted to analyse the relationship between the duration of lockdown and glycaemic targets & diabetes-related complications.
Results The predictive model was extremely robust (R2 = ) and predicted outcomes for period of lockdown up to 90 days. Chapter 7 Simple Linear Regression “All models are wrong, but some are useful.” — George E.
P. Box. After reading this chapter you will be able to: Understand the concept of a model. Describe two ways in which regression coefficients are derived.
Estimate and visualize a regression model using R.Regression analysis in practice with GRETL Prerequisites Which already shows that a linear relationship can be assumed between the price of houses and their area.
A graphical analysis may be useful as long as you have a two-variate problem. You can estimate a linear regression equation by OLS in the Model menu:File Size: 2MB.4 Linear Regression with One Regressor. This chapter introduces the basics in linear regression and shows how to perform regression analysis in linear regression, the aim is to model the relationship between a dependent variable \(Y\) and one or more explanatory variables denoted by \(X_1, X_2, \dots, X_k\).Following the book we will focus on the concept of simple linear regression.