A Methodology to Implement Box-Cox Transformation When No Covariate is Available

2014-01-01
Dag, Osman
Asar, Ozgur
İlk Dağ, Özlem
Box-Cox transformation is one of the most commonly used methodologies when data do not follow normal distribution. However, its use is restricted since it usually requires the availability of covariates. In this article, the use of a non-informative auxiliary variable is proposed for the implementation of Box-Cox transformation. Simulation studies are conducted to illustrate that the proposed approach is successful in attaining normality under different sample sizes and most of the distributions and in estimating transformation parameter for different sample sizes and mean-variance combinations. Methodology is illustrated on two real-life datasets.
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION

Suggestions

A computational approach to nonparametric regression: bootstrapping CMARS method
Yazici, Ceyda; Yerlikaya-Ozkurt, Fatma; Batmaz, İnci (2015-10-01)
Bootstrapping is a computer-intensive statistical method which treats the data set as a population and draws samples from it with replacement. This resampling method has wide application areas especially in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a special case of nonparametric regression, conic multivariate adaptive regression splines (CMARS), a statistical machin...
An Approximate MSE Expression for Maximum Likelihood and Other Implicitly Defined Estimators of Non-Random Parameters
Mehmetcik, Erdal; Orguner, Umut; Candan, Çağatay (2023-03-01)
An approximate mean square error (MSE) expression for the performance analysis of implicitly defined estimators of non-random parameters is proposed. An implicitly defined estimator (IDE) declares the minimizer/maximizer of a selected cost/reward function as the parameter estimate. The maximum likelihood (ML) and the least squares estimators are among the well known examples of this class. In this paper, an exact MSE expression for implicitly defined estimators with a symmetric and unimodal objective functi...
A Graph-Based Concept Discovery Method for n-Ary Relations
Abay, Nazmiye Ceren; MUTLU, ALEV; Karagöz, Pınar (2015-09-04)
Concept discovery is a multi-relational data mining task for inducing definitions of a specific relation in terms of other relations in the data set. Such learning tasks usually have to deal with large search spaces and hence have efficiency and scalability issues. In this paper, we present a hybrid approach that combines association rule mining methods and graph-based approaches to cope with these issues. The proposed method inputs the data in relational format, converts it into a graph representation, and...
An ilp-based concept discovery system for multi-relational data mining
Kavurucu, Yusuf; Karagöz, Pınar; Department of Computer Engineering (2009)
Multi Relational Data Mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Induct...
An fMRI segmentation method under markov random fields for brain decoding
Aksan, Emre; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015)
In this study, a specially tailored segmentation method for partitioning the fMRI data into a set of "homogenous" regions with respect to a predefined cost function is proposed. The proposed method, referred as f-MRF, employs univariate and multivariate fMRI data analysis techniques under Markov Random Fields to estimate the segments by resolving a mixture density. The univariate approach helps identifying activation pattern of a voxel independently from other voxels. In order to capture local interactions ...
Citation Formats
O. Dag, O. Asar, and Ö. İlk Dağ, “A Methodology to Implement Box-Cox Transformation When No Covariate is Available,” COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, pp. 1740–1759, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32826.