a forecasting technique used to establish the relationship between quantifiable variables. In regression analysis, data on dependent and independent variables is plotted on a scatter graph or diagram, and trends are indicated through a line of best fit. The use of a single independent variable is known as simple regression analysis, while the use of two or more independent variables is called multiple regression analysis.
This article, explains the mathematics of Sharpe’s algorithm. As it turns out, a fairly complete and mathematically rigorous description of the algorithm can be given without using a lot of mathematical formalism. William F. Sharpe's method of returns- based style analysis is substantially different from classical multivariate regression analysis. While...
Regression testing is an important part of software quality assurance. The authors work to extend regression testing to include regression benchmarking, which applies benchmarking to detect regressions in performance. Given the specific requirements of regression benchmarking, many contemporary benchmarks are not directly usable in regression benchmarking. To overcome this, the...
ABSTRACT This case study illustrates the analysis of two possible regression models for bivariate claims data. Estimates or forecasts of loss distributions under these two models are developed using two methods of analysis: (1) maximum likelihood estimation and (2) the Bayesian method. These methods are applied to two data sets...
A novel class of regression estimates characterized by local and global robustness is presented. The proposed estimates are based on two loss functions which can be selected to yield high breakdown point in simultaneous fashion together with weights that penalize high-leverage observations. Numerical studies and a Monte Carlo simulation show...
In this paper, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman p, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor...
This white paper report deals with Hierarchical linear models that are a generalization of Bayesian linear regression. They differ from Bayesian regression in that they determine the width of the prior distribution for the regression coefficients automatically from the data. They are particularly appropriate when we want to answer questions...
The research leading up to the publication of this article was conducted under the CPI initiative to expand the scope of developing hedonic regression models for quality adjustment purposes to more items within the CPI market basket. The primary focus of the article is to provide a detailed analysis of...
This case study defines how the company boost up its sales with the help of predict analysis and forecasting.New predictive tools from several vendors are being used by customers to gain and retain customers, improve efficiency of mail-order businesses, and even fight crime. The article also tells that...
Calibrating the scale factor of the NIST Ultraviolet UV Microscope requires determining the regression slope for UV Microscope measurements on a traceable reference standard. The uncertainty of this slope is the Type B uncertainty component for the traceability of the Standard Reference Material SRM 2800s calibrated on this instrument. This...
ABSTRACT: This case provides the opportunity to use various empirical techniques (i.e., high-low method, simple regression, and multiple regression) in the estimation of cost functions. The case uses the airline industry as the setting for this analysis and, in particular, focuses on the efforts of Delta...
The National Health Interview Survey is used to develop estimates of finite population variables for all of the American states, including the District of Columbia, and salient subpopulation within the said areas. Information from this survey are subjected to both Bayesian hierarchical analysis and multivariate linear regression methods. Cross-validation techniques...
Introduction to Nonparametric Regression by Kunio Takezawa. John Wiley & Sons, Inc., Hoboken, New Jersey, 2006. xviii + 568 pp. $110.THE SMOOTHING of data to discern an underlying curve or surface is a problem that has been with us as long as people have been examining time series or scatter...
The outcome prediction models using Artificial Neural Network ANN and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. This paper talks about a study which compares the performance of artificial neural network and multivariable logistic regression models,...
"With the widespread use of Six Sigma has come a renewed interest in developing statistical models and using regression analysis. The Six Sigma tools of multi-vari studies and design of experiments are used to develop models of this type. These tools rely heavily on the use of regression analysis to...
This paper provides evaluation on two Bayesian regression models for receiver operating characteristic ROC curve analysis of continuous diagnostic outcome data with covariates. The diagnostic tests were accurate in both examples. PSA levels were most accurate for staging prostate cancer among intermediate-risk patients. Stone size was predictive of treatment option...
Commercial land price gradients for an emerging real estate market are estimated using spatial regression techniques. Spatial statistics are used to explore the extent of spatial autocorrelation in the residuals of an OLS land price gradient model. Spatial autocorrelation is present but not to the same degree for all time...
We consider nonparametric regression in a longitudinal marginal model of generalized estimating equation GEE type with a time-varying covariate in the situation where the number of observations per subject is finite and the number of subjects is large. In such models, the basic shape of the regression function is affected...
An analytic framework is developed that couples a cash flow simulation model with regression analysis to construct numerical functionals associated with the fiscal regime. A meta-modeling approach is used to derive relationships that specify how the present value, rate of return, and take statistic vary as a function of the...
A new study titled "Lucky CEOs" presents the investigation of the opportunistic timing of stock option grants from 1996 through 2005 using regression analysis and other statistical methods. The results of the analysis are astounding, and they indicate that the ethics and governance reforms initiated by the Sarbanes-Oxley Act SOX...
This article analyzes the returns distribution of Hedge Funds strategies, the average returns obtained over the past ten years and their correlation with a traditional portfolio. The aim is to identify the characteristics of each Hedge Fund investment strategy in order to be able to construct an optimal Hedge Fund...
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