Applications of estimation techniques on genetic and other types of data

Download
143151.pdf
2003
Aslan, Murat
Show full item record
188
views
0
downloads
The parameters of genetic and other types of data, particularly with small samples, are estimated by using method of moments, least squares, minimum chi- square, maximum likelihood and modified maximum likelihood estimation methods. These methods are also compared in terms of their efficiencies and robustness property.
Linkage probability, Maximum likelihood estimation, Modified maximum likelihood estimation, Method of moments, Least square estimation, Minimum chi-square estimation, Robustness, Efficiency
https://hdl.handle.net/11511/13174
Graduate School of Natural and Applied Sciences, Thesis

Suggestions

Nonnormal regression. I. Skew distributions
İslam, Muhammed Qamarul; Yildirim, F (2001-01-01)
In a linear regression model of the type y = thetaX + e, it is often assumed that the random error e is normally distributed. In numerous situations, e.g., when y measures life times or reaction times, e typically has a skew distribution. We consider two important families of skew distributions, (a) Weibull with support IR: (0, infinity) on the real line, and (b) generalised logistic with support IR: (-infinity, infinity). Since the maximum likelihood estimators are intractable in these situations, we deriv...
Fine resolution frequency estimation from three DFT samples: Case of windowed data
Candan, Çağatay (2015-09-01)
An efficient and low complexity frequency estimation method based on the discrete Fourier transform (DFT) samples is described. The suggested method can operate with an arbitrary window function in the absence or presence of zero-padding. The frequency estimation performance of the suggested method is shown to follow the Cramer-Rao bound closely without any error floor due to estimator bias, even at exceptionally high signal-to-noise-ratio (SNR) values.
Non-normal bivariate distributions: estimation and hypothesis testing
Qunsiyeh, Sahar Botros; Tiku, Moti Lal; Department of Statistics (2007)
When using data for estimating the parameters in a bivariate distribution, the tradition is to assume that data comes from a bivariate normal distribution. If the distribution is not bivariate normal, which often is the case, the maximum likelihood (ML) estimators are intractable and the least square (LS) estimators are inefficient. Here, we consider two independent sets of bivariate data which come from non-normal populations. We consider two distinctive distributions: the marginal and the conditional dist...
Applications of hybrid discrete Fourier transform-moment method to the fast analysis of large rectangular dipole arrays printed on a thin grounded dielectric substrate
Chou, HT; Ko, HK; Aydın Çivi, Hatice Özlem; ERTÜRK, VAKUR BEHÇET (2002-08-05)
Recently a discrete Fourier transform-method of moments (DFT-MoM) scheme was developed for fast analysis of electrically large rectangular planar dipole arrays, which has been shown to be very efficient in terms of number reduction of unknown variables and computational complexity. The applications of this DFT-MoM to treat dipole arrays printed on a grounded dielectric substrate are examined in this Letter. Numerical results are presented to validate its efficiency and accuracy. (C) 2002 Wiley Periodicals, ...
Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression
Tan, Mehmet; Polat, Faruk; Alhajj, Reda (2009-11-04)
As the number of genes in a transcriptional regulatory network is large and the number of samples in biological data types is usually small, there is a need for integrating multiple data types for reverse engineering these networks. In this paper, we propose a method to integrate microarray gene expression, ChIP-chip and transcription factor binding motif data sets in a partial least squares regression model to derive transcription factors (TFs) gene interactions. Both single and synergistic effects of TFs ...
Citation Formats
M. Aslan, “Applications of estimation techniques on genetic and other types of data,” M.S. - Master of Science, Middle East Technical University, 2003.
METU IIS - Integrated Information Service open@metu.edu.tr