A new outlier detection method based on convex optimization: application to diagnosis of Parkinson's disease

Date

2020-12-01

Author

TAYLAN, PAKİZE
Yerlikaya-Ozkurt, Fatma
Bilgic Ucak, Burcu
Weber, Gerhard Wilhelm

Metadata

Show full item record

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

Item Usage Stats

237
views

0
downloads

Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability

URI

https://hdl.handle.net/11511/88507

Journal

JOURNAL OF APPLIED STATISTICS

DOI

https://doi.org/10.1080/02664763.2020.1864815

Collections

Graduate School of Applied Mathematics, Article

Suggestions

OpenMETU
Core

Long-tailed graphical model and frequentist inference of the model parameters for biological networks AĞRAZ, MELİH; Purutçuoğlu Gazi, Vilda (Informa UK Limited, 2020-03-12) The biological organism is a complex structure regulated by interactions of genes and proteins. Various linear and nonlinear models can define activations of these interactions. In this study, we have aimed to improve the Gaussian graphical model (GGM), which is one of the well-known probabilistic and parametric models describing steady-state activations of biological systems, and its inference based on the graphical lasso, shortly Glasso, method. Because, GGM with Glasso can have low accuracy when the syst...
Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation Weber, Gerhard Wilhelm; Taylan, P.; Alparslan-Gok, S. Z.; Oezoeguer-Akyuz, S.; Akteke-Ozturk, B. (Springer Science and Business Media LLC, 2008-12-01) This mathematical contribution is addressed towards the wide interface of life and human sciences that exists between biological and environmental information. Like very few other disciplines only, the modeling and prediction of genetical data is requesting mathematics nowadays to deeply understand its foundations. This need is even forced by the rapid changes in a world of globalization. Such a study has to include aspects of stability and tractability; the still existing limitations of modern technology i...
An evaluation of a novel approach for clustering genes with dissimilar replicates Cinar, Ozan; İyigün, Cem; İlk Dağ, Özlem (Informa UK Limited, 2020-12-01) Clustering the genes is a step in microarray studies which demands several considerations. First, the expression levels can be collected as time-series which should be accounted for appropriately. Furthermore, genes may behave differently in different biological replicates due to their genetic backgrounds. Highlighting such genes may deepen the study; however, it introduces further complexities for clustering. The third concern stems from the existence of a large amount of constant genes which demands a hea...
A marginalized multilevel model for bivariate longitudinal binary data Inan, Gul; İlk Dağ, Özlem (Springer Science and Business Media LLC, 2019-06-01) This study considers analysis of bivariate longitudinal binary data. We propose a model based on marginalized multilevel model framework. The proposed model consists of two levels such that the first level associates the marginal mean of responses with covariates through a logistic regression model and the second level includes subject/time specific random intercepts within a probit regression model. The covariance matrix of multiple correlated time-specific random intercepts for each subject is assumed to ...
Marginalized transition random effect models for multivariate longitudinal binary data İlk Dağ, Özlem (Wiley, 2007-03-01) Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999, 2002) to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within-subject, dependence, either...

Citation Formats

P. TAYLAN, F. Yerlikaya-Ozkurt, B. Bilgic Ucak, and G. W. Weber, “A new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s disease,” JOURNAL OF APPLIED STATISTICS, pp. 0–0, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/88507.