A contribution to modern data reduction techniques and their applications by applied mathematics and statistical learning

Download
2010
Sakarya, Hatice
Data Reduction Techniques, Locally Linear Embedding, Isomap, Principal Component Analysis.

Suggestions

Bayesian semiparametric models for nonignorable missing datamechanisms in logistic regression
Öztürk, Olcay; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2011)
In this thesis, Bayesian semiparametric models for the missing data mechanisms of nonignorably missing covariates in logistic regression are developed. In the missing data literature, fully parametric approach is used to model the nonignorable missing data mechanisms. In that approach, a probit or a logit link of the conditional probability of the covariate being missing is modeled as a linear combination of all variables including the missing covariate itself. However, nonignorably missing covariates may n...
A computational approach to nonparametric regression: bootstrapping cmars method
Yazıcı, Ceyda; Batmaz, İnci; Department of Statistics (2011)
Bootstrapping is a resampling technique which treats the original data set as a population and draws samples from it with replacement. This technique is widely used, especially, in mathematically intractable problems. In this study, it is used to obtain the empirical distributions of the parameters to determine whether they are statistically significant or not in a special case of nonparametric regression, Conic Multivariate Adaptive Regression Splines (CMARS). Here, the CMARS method, which uses conic quadr...
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...
Effect of estimation in goodness-of-fit tests
Eren, Emrah; Sürücü, Barış; Department of Statistics (2009)
In statistical analysis, distributional assumptions are needed to apply parametric procedures. Assumptions about underlying distribution should be true for accurate statistical inferences. Goodness-of-fit tests are used for checking the validity of the distributional assumptions. To apply some of the goodness-of-fit tests, the unknown population parameters are estimated. The null distributions of test statistics become complicated or depend on the unknown parameters if population parameters are replaced by ...
Pairwise multiple comparisons under short-tailed symmetric distribution
Balcı, Sibel; Akkaya, Ayşen; Department of Statistics (2007)
In this thesis, pairwise multiple comparisons and multiple comparisons with a control are studied when the observations have short-tailed symmetric distributions. Under non-normality, the testing procedure is given and Huber estimators, trimmed mean with winsorized standard deviation, modified maximum likelihood estimators and ordinary sample mean and sample variance used in this procedure are reviewed. Finally, robustness properties of the stated estimators are compared with each other and it is shown that...
Citation Formats
H. Sakarya, “A contribution to modern data reduction techniques and their applications by applied mathematics and statistical learning,” M.S. - Master of Science, Middle East Technical University, 2010.