Bayesian Approach to identify Masking and Swamping Problems in the Multivariate-Multiple Regression Analysis

Ekiz, Ufuk
April 2006
Gazi University Journal of Science;Apr2006, Vol. 19 Issue 2, p113
Academic Journal
The Bayesian method developed by Chaloner and Brant to see whether, for instance, the outlier observation is included in the linear model or not, focuses on posterior distributions of realized but unobserved errors [5]. This method has been expanded by Varbanov for the multivariate-multiple regression model in a way to provide analysis opportunities for separate observations in terms of their being an outlier [13]. This study expanded the proposed method by Varbanov to offer the opportunity for analyzing whether observations in groups are an outlier or not. Therefore, it is likely to claim the existence of masking and swamping problems.


Related Articles

  • RoPEUS: A New Robust Algorithm for Static Positioning in Ultrasonic Systems. Prieto, José Carlos; Croux, Christophe; Jiménez, Antonio Ramón // Sensors (14248220);2009, Vol. 9 Issue 6, p4211 

    A well known problem for precise positioning in real environments is the presence of outliers in the measurement sample. Its importance is even bigger in ultrasound based systems since this technology needs a direct line of sight between emitters and receivers. Standard techniques for outlier...

  • Sample Size Determination Based on Decision Rules in Regression Analysis. Xiulin Geng // Journal of Systems Science & Information;Mar2008, Vol. 6 Issue 1, p81 

    Sample inference is an important way for people to carry out awareness, which is based on sampling and the subsequent sample observation. In this process, in order to ensure the purpose of the studying sample observation can be achieved, we need know the sample size in advance. Generally...

  • spikeslab: Prediction and Variable Selection Using Spike and Slab Regression. Ishwaran, Hemant; Kogalur, Udaya B.; Rao, J. Sunil // R Journal;Dec2010, Vol. 2 Issue 2, p68 

    Weighted generalized ridge regression offers unique advantages in correlated highdimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used...

  • Comparison of Hypothesis Testing and Bayesian Model Selection. Hoijtink, Herbert; Klugkist, Irene // Quality & Quantity;Feb2007, Vol. 41 Issue 1, p73 

    The main goal of both Bayesian model selection and classical hypotheses testing is to make inferences with respect to the state of affairs in a population of interest. The main differences between both approaches are the explicit use of prior information by Bayesians, and the explicit use of...

  • ANALYSIS OF NONLINEAR REGRESSION MODELS: A CAUTIONARY NOTE. Peddada, Shyamal D.; Haseman, Joseph K. // Dose-Response;2005, Vol. 3 Issue 3, p342 

    Regression models are routinely used in many applied sciences for describing the relationship between a response variable and an independent variable. Statistical inferences on the regression parameters are often performed using the maximum likelihood estimators (MLE). In the case of nonlinear...

  • Probing Three-Way Interactions in Moderated Multiple Regression: Development and Application of a Slope Difference Test. Dawson, Jeremy F.; Richter, Andreas W. // Journal of Applied Psychology;Jul2006, Vol. 91 Issue 4, p917 

    Researchers often use 3-way interactions in moderated multiple regression analysis to test the joint effect of 3 independent variables on a dependent variable. However, further probing of significant interaction terms varies considerably and is sometimes error prone. The authors developed a...

  • KUKLA DEĞİŞKENLERÄ°N T Ä°STATÄ°STİĞİ Ä°LE AYKIRI GÖZLEMLER TESPÄ°T EDÄ°LEMEZ. KİRACI, Arzdar // Istanbul University Econometrics & Statistics e-Journal;2011, Vol. 15 Issue 1, p1 

    In the current literature, in order to be able to detect a single observation as an outlier observation, this observation is represented by a dummy variable and the dummy variable is checked for statistical significance. For an observation to be an outlier observation, the thesis of significant...

  • Quality assurance challenge 9. Reichenbächer, Manfred; Einax, Jürgen W. // Analytical & Bioanalytical Chemistry;Mar2010, Vol. 396 Issue 5, p1627 

    The article discusses the outlier test in regression analysis for quality assurance. An outlier is considered as a value that is outside the confidence interval of the linear regression function. The tests used for straight line regression are explained, including the F test and the t test,...

  • Detection of Outliers and Patches in Bilinear Time Series Models. Ping Chen; Ling Li; Ye Liu; Jin-Guan Lin // Mathematical Problems in Engineering;2010, Vol. 2010, Special section p1 

    We propose a Gibbs sampling algorithm to detect additive outliers and patches of outliers in bilinear time series models based on Bayesian view. We first derive the conditional posterior distributions, and then use the results of first Gibbs run to start the second adaptive Gibbs sampling. It is...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics