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VOLATILITY ESTIMATION IN MISSING AT RANDOM HIGH-FREQUENCY FINANCIAL TIME SERIES
(Applied Statistics, 2023 , Master Thesis)
More than 15 years ago, the capital markets have seen significant development, introducing high-frequency trading and a shift of market towards high-frequency and algorithm trading. It was always believed that high-frequency ...
IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION OF BONE MINERAL DENSITY TYPES BASED ON QATAR BIOBANK DATA
(Applied Statistics, 2023 , Master Thesis)
Bone Mineral Density (BMD) test measures the amount of calcium and other minerals in specific areas of bone. Low BMD is a well-known problem and results in bone fractures in millions of people around the world. BMD can be ...
A COMPARISON STUDY: EVALUATING SOME STATISTICAL AND AI TECHNIQUES FOR MEDICAL APPLICATION
(Applied Statistics, 2025 , Master Thesis)
Analyzing medical data using artificial intelligence and statistical techniques may contribute to improving healthcare by helping to accurately identify important and irrelevant features in data collection and disease ...
IMAGE MONITORING USING MULTIVARIATE CONTROL CHARTS
(Applied Statistics, 2025 , Master Thesis)
This study focuses on developing a Multivariate Hotelling's T2 control chart to monitor image data. Dimensionality reduction of the image data is achieved through Principal Component Analysis (PCA) and Sparse Principal ...
COMPETING RISK MODELS IN PRESENCE OF PROGRESSIVELY TYPE-II CENSORED DATA FOR DAGUM DISTRIBUTION
(Applied Statistics, 2024 , Master Thesis)
In the survival time analysis, there could be more than one cause of failure for an individual or an item. Usually, researchers are interested in survival times under a certain cause of failure, considering the rest of the ...
ON THE GAUSSIAN PROCESS FOR STATIONARY AND NON-STATIONARY TIME SERIES PREDICTION FOR THE QATAR STOCK MARKET
(Applied Statistics, 2024 , Master Thesis)
This research adopts a Gaussian prediction model for non-stationary time series. Then, we discuss four transformation techniques: Generalized Optimal Wavelet Decomposition Algorithm (GOWDA), Hilbert Huang transform (HHT), ...
Parameter Estimation and Prediction of Future Failures in the Log-Logistic Distributions Based on Hybrid-Censored Data
(Applied Statistics, 2020 , Master Thesis)
The main purpose of this thesis is to study the prediction of future observations of a
Log-Logistic distribution from Hybrid Censored Samples. We will study parameter
point estimation, interval estimation, different ...
Diagnostic Checking For Linearity in Time Series Models
(Applied Statistics, 2020 , Master Thesis)
In this thesis, I studied the well-known portmanteau tests appearing in the time
series literature. In particular, I interest in reviewing the test statistics that can be used
to check the adequacy of the fitted ...
Goodness of Fit Testing for the Log-Logistic distribution Based on Type I Censored Data
(Applied Statistics, 2021 , Master Thesis)
The main aim of this thesis is to investigate the problem of the goodness of fit
test for Log-Logistic distribution based on empirical distribution function under Type
I censored data. The maximum likelihood estimation ...
Exponential model for breast cancer partly interval censored data via multiple imputation
(Applied Statistics, 2021 , Master Thesis)
The estimation problem for interval-censored data has been investigated by several
authors. The application of conventional methods to interval censored data that has been
considered by Lindsey and Ryan (1998) showed ...