Bivariate and Multivariate Data Cloning through Non Linear Regression Models
Abstract
Abstract Views: 0Nonlinear regression analysis holds significant popularity in mathematical, engineering, and social science domains. Disciplines like financial matters, biology, and natural chemistry have broadly utilized nonlinear regression models (NLRMs). Cloned datasets have their own importance in such areas which provide the same fit of bivariate and multivariate nonlinear regression models for the actual datasets. This article presents a sequence of cloned datasets that give exactly the same fit of bivariate and multivariate nonlinear regression models.
Downloads
References
Anscombe FJ. Graphs in statistical analysis. Am Statistic. 1973;27(1):17–21.
Chatterjee S, Firat A. Generating data with identical statistics but dissimilar graphics: A follow up to the Anscombe dataset. Am Stat. 2007;61(3):248–254. https://doi.org/10.1198/000313007X220057
Govindaraju K, Haslett SJ. Illustration of regression towards the mean. Int J Matim Edu Sci Technol. 2008;39(4):544–550. https://doi.org/10.1080/00207390701753788
Haslett SJ, Govindaraju K. Cloning data: Generating datasets with exactly the same multiple linear regression fit. Aust New Zealand J Stat. 2009;51(4):499–503. https://doi.org/10.1111/j.1467-842X.2009.00560.x
Lele SR, Dennis B, Lutscher F. Data Cloning: Easy maximum likelihood estimation for complex ecological models using bayesian markov chian monte carlo methods. Ecol Lett. 2007;10:551–563. https://doi.org/10.1111/j.1461-0248.2007.01047.x
Lele SR, Nadeem K, Schmuland B. Estimability and likelihood inference for generalized linear mixed models using data cloning. J Am Stat Assoc. 2010;105:1617–1625. https://doi.org/10.1198/jasa.2010.tm09757
Jacquier E, Johannes M, Polson N. MCMC Maximum likelihood for latent state models. J Econom. 2007;137:615–640. https://doi.org/10.1016/j.jeconom.2005.11.017
Fung BCM, Wang K, Chen R, Yu PS. Privacy-preserving data publishing: A survey of recent developments. ACM Comput Surv. 2010;42(4):e14. https://doi.org/10.1145/1749603.1749605
Haslett SJ, Govindaraju K. Data cloning: Data visualization, smoothing, confidentiality, and encryption. J Stat Plan Infer. 2012;142:410–422. https://doi.org/10.1016/j.jspi.2011.07.020
Ponciano JM, Burleigh JG, Braun EL, Taper ML. Assessing parameter identifiability in phylogenetic models using data cloning. Syst Biol. 2012;61(6):955–972. https://doi.org/10.1093/sysbio/sys055
Amvrosiadis G, Bhadkamkar M. Identifying trends in enterprisedata protection systems. Paper presented at: USENIX Annual Technical Conference; July 8–10, 2015; Santa Clara, USA.
Download the Datasaurus: Never trust summary statistics alone; always visualize your data. Cairo website. http://www.thefunctionalart.com/2016/08/downloaddatasaurus-never-trust-summary.html
Matejka J, Fitzmaurice G. Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. Paper presented at: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems; May 6–11, 2017; Denver Colorado, USA. https://doi.org/10.1145/3025453.3025912
Mao X, Ruiz E, Veiga H. Threshold stochastic volatility: Properties and forecasting. Int J Forec, 2017;33(4):1105–1123. https://doi.org/10.1016/j.ijforecast.2017.07.001
Mao X, Ruiz E, Veiga H, Czellar V. Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation. Econom Stat. 2020;13:84–105. https://doi.org/10.1016/j.ecosta.2019.08.002
Hussain S, Daniyal M, Ogundokun RO, Muhammad YS, Iqbal Z, Ahmed, R. Cloning data with unchanged estimates of estimable non-linear functions of parameters. F1000Research. 2022;10:e106. https://doi.org/10.12688/f1000research.28297.2
Copyright (c) 2023 Dr. Sajid Hussain, Rashid Ahmed
This work is licensed under a Creative Commons Attribution 4.0 International License.