الفهرس 
CIRCULAR REGRESSION MODELSOnly 14 pages are availabe for public view
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Abstract
In most model selection problems the number of parameters should be large and grow with the sample size. Circular data as any other types of data are subjected to contaminate with some unexpected observations. Outliers in the context of circular data would be defined as a set of observations which is inconsistent with the rest of the sample. It is expected to lay far from the mean direction of the circular sample.
We studied two methods to detect outliers in circular data such as kernel density function (KDF) and penalized maximum likelihood (PML). The first method discussed an outlier in circular data using kernel density function (KDF) with experiments of two datasets and identify the points as a cluster points and this being an outlier. Local Outlier Factor (LOF), which is based on the density estimate theory play a basic algorithm, however, LOF has two disadvantages that restrict its performance in outlier detection. Besides, we propose an algorithm which is more suitable for outlier detection in circular data by the local density factor (LDF). We discuss this algorithm for outlier detection which find the outliers by comparing the local density of each point to the local density of its neighbors in circular data.
On the other hand, the second method detect an outliers in logistic circular data with algorithms and R program. In this method, we have a rainfall data as an application for this model. In penalized maximum likelihood (PML), we develop some methods from linear model and convert these method in circular data as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). We use local linear approximation (LLA) algorithm to approximate the parameters in logistic regression model for circular data which is intended to describe the relationship between a binary response and circular predictor(s).
The research contains of English and Arabic summary, a list of abbreviations and a list of references. It also outlines in five chapters as follows:
Chapter 1: Introduction
We give a history of circular data arise in various ways. Although, we saw an application of circular data in widely differing scientific disciplines. We present a review on different methods of identification of outliers in linear regression which has the possibility to be extended to the model involving circular regression models.
Chapter 2: Circular Regression Models
This chapter studies some types of circular regression models such as circular-linear regression model, circular-circular regression model, multiple circular regression model and multivariate-multiple circular regression model and we give properties of each model.
Chapter 3: Outliers in Different Models
In this chapter, we discuss an outlier in different models. Outlier detection in linear regression model describe some methods as residual analyzes using standardized residuals, row deletion methods, hat matrix, outlier detection based on robust regression and forward search. In outlier detection in circular regression model, we study an outlier in COVRATIO statistic, the mean circular error statistic, DM circular regression model and DFBETAc statistic.
Chapter 4: Detection of Outliers in Circular Data using Kernel Density Func¬tion
We present a new method to detect outliers in circular data. This method is called kernel density function (KDF) which is classical topics in nonparametric statistics. We study a parameter estimation and kernel function and motivate these for the circular kernel density estimator. The proposed method modify by an experiment of two datasets which are generated by von Mises distribution and different in sizes and properties.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.
Chapter 5: Detection of Outliers in Logistic Circular Regression via Penalized Maximum Likelihood
We detect outliers in logistic circular regression using an alternative method which called penalized maximum likelihood (PML). We discuss a parameter estimation and penalty func¬tion and there are methods of choice penalty function such as penalized least squares (PLS), smoothly clipped absolute deviation (SCAD) and new unified algorithm (NUA). Outlier detection in logistic circular data is an important in thesis. Our simulation use circular pre¬dictor generated by von Mises distribution with mean direction and concentration parameter. Real example is a rainfall data.