Gul Inan

Bayesian Learning and Relevance Vector Machines Approach for Downscaling of Monthly Precipitation

AbstractIn this study, statistical downscaling of large-scale general circulation model (GCM) simulations to monthly precipitation of Kemer Dam, in Turkey, has been performed through relevance vector machines (RVMs). All possible regression methods along with statistical measures have been used to select potential predictors through reanalysis data providing air850, hgt850, and prate variables as the optimal. The determined explanatory variables are then used for training RVM-based statistical downscaling model. A least-squares support vector machine (LSSVM)-based downscaling model is also constructed to compare the downscaling performance of RVM through some performance evaluation measures such as R2, AdjR2 and RMS error (RMSE). Because RVM is able to obtain the better modeling accuracy in terms of all performance measures during the testing period, third-generation coupled climate model (CGCM3) simulations run through the trained RVM to obtain future scenario results. The effectiveness of the RVM model ...

Joint Generation of Binary, Ordinal, Count, and Normal Data with Specified Marginal and Association Structures in Monte-Carlo Simulations

This chapter is concerned with building a unified framework for concurrently generating data sets that include all four major kinds of variables (i.e., binary, ordinal, count, and normal) when the marginal distributions and a feasible association structure are specified for simulation purposes. The simulation paradigm has been commonly employed in a wide spectrum of research fields including the physical, medical, social, and managerial sciences. A central aspect of every simulation study is the quantification of the model components and parameters that jointly define a scientific process. When this quantification cannot be performed via deterministic tools, researchers resort to random number generation (RNG) in finding simulation-based answers to address the stochastic nature of the problem. Although many RNG algorithms have appeared in the literature, a major limitation is that they were not designed to concurrently accommodate all variable types mentioned above. Thus, these algorithms provide only an incomplete solution, as real data sets include variables of different kinds. This work represents an important augmentation of the existing methods as it is a systematic attempt and comprehensive investigation for mixed data generation. We provide an algorithm that is designed for generating data of mixed marginals, illustrate its logistical, operational, and computational details; and present ideas on how it can be extended to span more complicated distributional settings in terms of a broader range of marginals and associational quantities.

Power Analysis of C-TDT for Small Sample Size Genome-Wide Association Studies by the Joint Use of Case-Parent Trios and Pairs

In family-based genetic association studies, it is possible to encounter missing genotype information for one of the parents. This leads to a study consisting of both case-parent trios and case-parent pairs. One of the approaches to this problem is permutation-based combined transmission disequilibrium test statistic. However, it is still unknown how powerful this test statistic is with small sample sizes. In this paper, a simulation study is carried out to estimate the power and false positive rate of this test across different sample sizes for a family-based genome-wide association study. It is observed that a statistical power of over 80% and a reasonable false positive rate estimate can be achieved even with a combination of 50 trios and 30 pairs when 2% of the SNPs are assumed to be associated. Moreover, even smaller samples provide high power when smaller percentages of SNPs are associated with the disease.