Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies

Date

2017-01-01

Author

Demirtas, Hakan
Vardar Acar, Ceren

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

85
views

0
downloads

This chapter is concerned with the assessment of correlational magnitude changes when a subset of the continuous variables that may marginally or jointly follow nearly any distribution in a multivariate setting is dichotomized or ordinalized. Statisticians generally regard discretization as a bad idea on the grounds of power, information, and effect size loss. Despite this undeniable disadvantage and legitimate criticism, its widespread use in social, behavioral, and medical sciences stems from the fact that discretization could yield simpler, more interpretable, and understandable conclusions, especially when large audiences are targeted for the dissemination of the research outcomes. We do not intend to attach any negative or positive connotations to discretization, nor do we take a position of advocacy for or against it. The purpose of the current chapter is providing a conceptual framework and computational algorithms for modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. Both directions (identification of the pre-discretization correlation value in order to attain a specified post-discretization magnitude, and the other way around) are discussed. The ideas are developed for bivariate settings; a natural extension to the multivariate case is straightforward by assembling the individual correlation entries. The paradigm under consideration has important implications and broad applicability in the stochastic simulation and random number generation worlds. The proposed algorithms are illustrated by several examples; feasibility and performance of the methods are demonstrated by a simulation study.

URI

https://hdl.handle.net/11511/92389

Journal

MONTE-CARLO SIMULATION-BASED STATISTICAL MODELING

DOI

https://doi.org/10.1007/978-981-10-3307-0_4

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models Yozgatlıgil, Ceylan (Informa UK Limited, 2009-11-01) Time series often contain observations of several variables and multivariate time series models are used to represent the relationship between these variables. There are many studies on vector autoregressive moving average (VARMA) models, but the representation of multiplicative seasonal VARMA models has not been seriously studied. In a multiplicative vector model, such as a seasonal VARMA model, the representation is not unique because of the noncommutative property of matrix multiplication. In this articl...
Algorithm Overview and Design for Mixed Effects Models Koca, Burcu; Gökalp Yavuz, Fulya (2021-06-06) Linear Mixed Model (LMM) is an extended regression method that is used for longitudinal data which has repeated measures within the individual. It is natural to expect high correlation between these repeats over a period of time for the same individual. Since classical approaches may fail to cover these correlations, LMM handles this significant concern by introducing random effect terms in the model. Besides its flexible structure in terms of modeling, LMM has several application areas such as clinical tri...
Estimation and hypothesis testing in multivariate linear regression models under non normality İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01) This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Practical Implementation of Generalized Force Vectors for the Multimodal Pushover Analysis of Building Structures Alici, F. Soner; Sucuoğlu, Haluk (SAGE Publications, 2015-05-01) A practical implementation of generalized multimodal pushover analysis is presented in this study, where the number of pushovers is reduced significantly in view of the number of modes contributing to seismic response. It has been demonstrated in two case studies that the reduced procedure for generalized pushover analysis is equally successful in estimating the maximum member deformations and forces under a ground excitation with reference to nonlinear response history analysis. It is further shown that th...
Quantitative measure of observability for linear stochastic systems Subasi, Yuksel; Demirekler, Mübeccel (Elsevier BV, 2014-06-01) In this study we define a new observability measure for stochastic systems: the mutual information between the state sequence and the corresponding measurement sequence for a given time horizon. Although the definition is given for a general system representation, the paper focuses on the linear time invariant Gaussian case. Some basic analytical results are derived for this special case. The measure is extended to the observability of a subspace of the state space, specifically an individual state and/or t...

Citation Formats

H. Demirtas and C. Vardar Acar, “Anatomy of Correlational Magnitude Transformations in Latency and Discretization Contexts in Monte-Carlo Studies,” MONTE-CARLO SIMULATION-BASED STATISTICAL MODELING, pp. 59–84, 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/92389.