Ahmet Sezer

MLR and ANN models of significant wave height on the west coast of India

Multiple linear regression (MLR) and artificial neural network (ANN) models are used in the present work to describe the significant wave height off Goa, located in the west Indian coast. A comparison study was carried out with the purpose of verifying when the artificial neural network and multiple linear regression models are appropriate for prediction of the significant wave height. Discussions of advantages and disadvantages are given in different point of view for both the methods. Several meteorological factors are used during the analysis and the ones affecting more to the model are kept. We concluded that non-linear models with wind speed and wind gust at a previous time step and air pressure, water temperature and air temperature at the same time step yield to better significant wave height models.

Multiple Comparison of Age Groups in Bone Mineral Density under Heteroscedasticity

Osteoporosis is a silent disease because individuals may not know that they have osteoporosis until their bones become so fragile. Bone mineral density (BMD) test helps to detect osteoporosis and determine the risk fractures. This study covers bone measurement data from total body dual energy X-ray absorptiometry scans for 28,454 persons who participated in the 1996–2006 National Health and Nutrition Examination Survey in USA Dual energy X-ray absorptiometry (DXA) method is known as the primary method for detecting osteoporosis because of its high precision and accuracy. Testing the equality of the means of normal populations when the variances are unknown and unequal is a fundamental problem in clinical trials and biomedical research. In this study we compare age groups based upon BMD in case of unequal variance being present among the groups. First we test equality of variances among the age groups by the Hartley test. And then Scott-Smith test is used to test equality of BMD means for the age groups. Finally, Tukey-Cramer confidence intervals are constructed to detect which groups start to differ from the reference group in which BMD reaches the peak level.

Comparison of confidence intervals for the Behrens-Fisher problem

ABSTRACT The Behrens–Fisher problem concerns the inferences for the difference between means of two independent normal populations without the assumption of equality of variances. In this article, we compare three approximate confidence intervals and a generalized confidence interval for the Behrens–Fisher problem. We also show how to obtain simultaneous confidence intervals for the three population case (analysis of variance, ANOVA) by the Bonferroni correction factor. We conduct an extensive simulation study to evaluate these methods in respect to their type I error rate, power, expected confidence interval width, and coverage probability. Finally, the considered methods are applied to two real dataset.

A Simulation Study on Generalized Pareto Mixture Model

The Generalized Pareto Distribution is commonly used for extreme value problems. Especially, the values which exceed the finite threshold, is the focus in extreme value problems like in insurance sector. The Generalized Pareto Distribution is well approach for modeling the samples which include these extreme values. In the real life, samples are heterogeneous. In such cases, the mixture models are better way for modeling the data. In this study, we generate random samples from the Generalized Pareto Mixture Distribution for modeling of heterogeneous data. For this purpose, we use two different Generalized Pareto Distribution as components of the Generalized Pareto Mixture Distribution. For generating random samples, The Inverse Transformation Method is used in the simulation study. The parameters of the mixture models are shape, scale and location are fixed. After generating random samples, Chi-Square Goodness-of-Fit Test is used for checking whether the generated samples are distributed based on the Generalized Pareto Distribution. R-Statistical Programming Language is used in simulation study.

Statistical power of an information-based test and its application to wave height data

Modeling wave heights is crucial for many maritime applications. An appropriate statistical distribution for describing wave heights is the Rayleigh distribution. Estimating and testing the significance of the parameters of a distribution are important in statistical modeling and allow meaningful predictions about uncertain events. We propose an information-based method to test the significance of parameters of a given one-dimensional distribution. The power of the proposed test is compared to that of the likelihood ratio tests for hypotheses on the parameters of the exponential and Rayleigh distributions. Monte Carlo simulations demonstrate that the proposed method yields a satisfactory power level that is comparable to that of the likelihood ratio test. The method is illustrated using real wave height data.