Robust estimation and hypothesis testing in microarray analysis

Ülgen, Burçin Emre
Microarray technology allows the simultaneous measurement of thousands of gene expressions simultaneously. As a result of this, many statistical methods emerged for identifying differentially expressed genes. Kerr et al. (2001) proposed analysis of variance (ANOVA) procedure for the analysis of gene expression data. Their estimators are based on the assumption of normality, however the parameter estimates and residuals from this analysis are notably heavier-tailed than normal as they commented. Since non-normality complicates the data analysis and results in inefficient estimators, it is very important to develop statistical procedures which are efficient and robust. For this reason, in this work, we use Modified Maximum Likelihood (MML) and Adaptive Maximum Likelihood estimation method (Tiku and Suresh, 1992) and show that MML and AMML estimators are more efficient and robust. In our study we compared MML and AMML method with widely used statistical analysis methods via simulations and real microarray data sets.


Deep Learning in the Presence of Label Noise: A Meta-Learning Approach
Algan, Görkem; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2021-3-12)
Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to practical challenges. Because of these practical challenges, label noise is a common problem in real-world datasets. This thesis presents two novel label noise robust learning algorithms: MSLG (Meta Soft Label Generation) and MetaLabelNet. Both al...
Robust Attitude Estimation Using IMU-Only Measurements
Candan, Batu; Söken, Halil Ersin (2021-01-01)
© 1963-2012 IEEE.This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning proce...
Özkurt, Tolga Esat; Hirschmann, Jan; Schnitzler, Alfons (2015-09-04)
Neural oscillations in various distinct frequency bands and their interrelations yield high temporal resolution signatures of the human brain activity. This study demonstrates solutions to some of the common challenges in the analysis of neurophysiological data by means of subthalamic local field potentials (LFP) acquired form patients with Parkinson's Disease (PD) undergoing deep brain stimulation therapy. Multivariate empirical mode decomposition (MEMD), being a data-driven method suitable for multichanne...
Neural network based orbit prediction for a geostationary satellite
Kutay, Ali Türker; Tulunay, Ersin; Tekinalp, Ozan (null; 2001-05-23)
An artificial Neural Network (NN) model was developed to estimate the semi-major axis (a), the eccentricity (e) and the inclination (i) of a geostationary satellite orbit. To facilitate a comparison between the NN model developed herewith and a real case, the TORKSAT lB geostationary satellite has been taken as example. A code that numerically solves the parameters of the TORKSAT's orbit, namely METUAEE1, is used to generate the training data for the NN model and to evaluate its performance. A Multi-La...
Robust Estimation of UAV Dynamics in the Presence of Measurement Faults
Hacızade, Cengiz; Söken, Halil Ersin (American Society of Civil Engineers (ASCE), 2012-01-01)
This study introduces a robust Kalman filter (RKF) with a filter-gain correction for cases of measurement malfunctions. Using defined variables called measurement-noise scale factors, the faulty measurements are taken into consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate ones. In this study, RKF algorithms with single and multiple scale factors are proposed and applied for the state estimation process of an unmanned aerial vehicle (UAV...
Citation Formats
B. E. Ülgen, “Robust estimation and hypothesis testing in microarray analysis,” Ph.D. - Doctoral Program, Middle East Technical University, 2010.