Ip Ching-Tak. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Time Series Analysis with Outliers --- p.1 / Chapter 2 --- Outlier Analysis in Time Series --- p.4 / Chapter 2.1 --- Basic Idea --- p.4 / Chapter 2.2 --- Outliers in Time Series --- p.6 / Chapter 2.2.1 --- One Outlier Case --- p.6 / Chapter 2.2.2 --- Multiple Outliers Case --- p.8 / Chapter 2.3 --- Outlier Identification --- p.9 / Chapter 2.3.1 --- Outlier Detection of One Outlier Case --- p.9 / Chapter 2.3.2 --- Case of Unknown Model Parameters --- p.10 / Chapter 2.3.3 --- Iterative Identification Procedure --- p.10 / Chapter 3 --- ARMA Model Forecasting --- p.13 / Chapter 3.1 --- Unknown Model Problem --- p.13 / Chapter 3.1.1 --- AR Approximation --- p.14 / Chapter 3.1.2 --- ARMA Approximation --- p.15 / Chapter 3.1.3 --- "Comparison of AIC, AICC and BIC" --- p.16 / Chapter 3.2 --- A Simulation Study --- p.19 / Chapter 3.2.1 --- Results for One-Step-Ahead Forecast --- p.20 / Chapter 3.2.2 --- Results for the Mean of Multiple Forecasts --- p.22 / Chapter 4 --- ARIMA Model Forecasting --- p.24 / Chapter 4.1 --- Effect of Differencing on Time Series --- p.24 / Chapter 4.1.1 --- Outlier Free Model --- p.24 / Chapter 4.1.2 --- Outlier Model --- p.25 / Chapter 4.2 --- Unknown Model Problem --- p.28 / Chapter 4.2.1 --- AR Approximation --- p.28 / Chapter 4.2.2 --- ARMA Approximation --- p.28 / Chapter 4.3 --- Unknown Differencing Case --- p.29 / Chapter 4.4 --- A Simulation Study --- p.29 / Chapter 4.4.1 --- Results for One-Step-Ahead Forecast --- p.30 / Chapter 4.4.2 --- Results for the Mean of Multiple Forecasts --- p.32 / Chapter 5 --- Illustrative Examples --- p.34 / Chapter 5.1 --- Examples of Stationary Time Series --- p.34 / Chapter 5.1.1 --- Example 1 --- p.34 / Chapter 5.1.2 --- Example 2 --- p.36 / Chapter 5.2 --- Examples of Nonstationary Time Series --- p.37 / Chapter 5.2.1 --- Example 3 --- p.37 / Chapter 5.2.2 --- Example 4 --- p.38 / Chapter 6 --- Conclusion --- p.40 / Chapter A --- "Comparison of AIC, AICC and BIC" --- p.42 / Chapter A.1 --- AR Approximation Results --- p.42 / Chapter A.2 --- ARMA Approximation Results --- p.45 / Chapter B --- Simulation Results for ARMA Models --- p.47 / Chapter C --- Simulation Results for ARIMA Models --- p.56 / Chapter D --- SACF and SPACF of Examples --- p.65 / Bibliography --- p.68

一個社會經濟學的目標變量，經常存在兩種不同收集頻率的數據。由於較低頻率的一組數據通常由大型普查中所獲得，其準確度及可靠性會較高。因此較低頻率的一組數據一般會視作基準，用作對頻率較高的另一組數據進行修正。 / 在基準修正過程中，一般會假設調查誤差及目標數據的大小互相獨立，即「累加模型」。然而，現實中兩者通常是相關的，目標變量越大，調查誤差亦會越大，即「乘積模型」。對此問題，陳兆國及胡家浩提出了利用準線性回歸手法對乘積模型進行基準修正。在本論文中，假設調查誤差服從AR(1)模型，首先我們會示範如何利用準線性回歸手法及默認調查誤差模型進行基準數據修正。然後，運用基準預測的方式，提出一個對調查誤差模型的估計辦法。最後我們會比較兩者的表現以及一些選擇誤差模型的指引。 / For a target socio-economic variable, two sources of data with different collecting frequencies may be available in survey data analysis. In general, due to the difference of sample size or the data source, two sets of data do not agree with each other. Usually, the more frequent observations are less reliable, and the less frequent observations are much more accurate. In benchmarking problem, the less frequent observations can be treated as benchmarks, and will be used to adjust the higher frequent data. / In the common benchmarking setting, the survey error and the target variable are always assumed to be independent (Additive case). However, in reality, they should be correlated (Multiplicative case). The larger the variable, the larger the survey error. To deal with this problem, Chen and Wu (2006) proposed a regression method called quasi-linear regression for the multiplicative case. In this paper, by assuming the survey error to be an AR(1) model, we will demonstrate the benchmarking procedure using default error model for the quasi-linear regression. Also an error modelling procedure using benchmark forecast method will be proposed. Finally, we will compare the performance of the default error model with the fitted error model. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Luk, Wing Pan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 56-57). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Recent Development For Benchmarking Methods --- p.2 / Chapter 1.2 --- Multiplicative Case And Benchmarking Problem --- p.3 / Chapter 2 --- Benchmarking With Quasi-linear Regression --- p.8 / Chapter 2.1 --- Iterative Procedure For Quasi-linear Regression --- p.9 / Chapter 2.2 --- Prediction Using Default Value φ --- p.16 / Chapter 2.3 --- Performance Of Using Default Error Model --- p.17 / Chapter 3 --- Estimation Of φ Via BM Forecasting method --- p.26 / Chapter 3.1 --- Benchmark Forecasting Method --- p.26 / Chapter 3.2 --- Performance Of Benchmark Forecasting Method --- p.28 / Chapter 4 --- Benchmarking By The Estimated Value --- p.34 / Chapter 4.1 --- Benchmarking With The Estimated Error Model --- p.35 / Chapter 4.2 --- Performance Of Using Estimated Error Model --- p.36 / Chapter 4.3 --- Suggestions For Selecting Error Model --- p.45 / Chapter 5 --- Fitting AR(1) Model For Non-AR(1) Error --- p.47 / Chapter 5.1 --- Settings For Non-AR(1) Model --- p.47 / Chapter 5.2 --- Simulation Studies --- p.48 / Chapter 6 --- An Illustrative Example: The Canada Total Retail Trade Se-ries --- p.50 / Chapter 7 --- Conclusion --- p.54 / Bibliography --- p.56

Hung, King Chi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 78-52). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Non-linear Time Series Models and Their Applications --- p.2 / Chapter 1.2 --- The SETAR Model --- p.4 / Chapter 1.3 --- Objectives and Organization of the Thesis --- p.6 / Chapter 2 --- The SETAR Model and Robust Test for Non-linearity --- p.8 / Chapter 2.1 --- A Brief Review of Existing Tests for Threshold-type Non-linearity --- p.9 / Chapter 2.2 --- Robust Tests for Threshold-type Non-linearity --- p.11 / Chapter 2.2.1 --- Tsay´ةs F Test --- p.12 / Chapter 2.2.2 --- The Proposed Test --- p.15 / Chapter 2.3 --- The Choice of the ψ-function --- p.23 / Chapter 2.4 --- A Simulation Study --- p.26 / Chapter 2.4.1 --- Data Generation Process (DGP) --- p.26 / Chapter 2.4.2 --- Simulation Findings --- p.29 / Chapter 3 --- Robust Estimation and Asymptotic Properties --- p.34 / Chapter 3.1 --- Least Squares Estimation --- p.37 / Chapter 3.2 --- Robust Estimation --- p.38 / Chapter 3.2.1 --- Asymptotic Properties --- p.40 / Chapter 3.3 --- A Simulation Study --- p.52 / Chapter 3.3.1 --- Data Generation Process (DGP) --- p.53 / Chapter 3.3.2 --- Simulation Findings --- p.55 / Chapter 3.3.3 --- Objective Function over r --- p.56 / Chapter 4 --- Numerical Example --- p.67 / Chapter 4.1 --- Methodology --- p.68 / Chapter 4.2 --- ASEAN Background --- p.69 / Chapter 4.2.1 --- Non-linearity tests on ASEAN Exchange Rate --- p.72 / Chapter 4.2.2 --- Estimation of the Return of Singaporean Dollar --- p.73 / Chapter 5 --- Conclusions and Further Research --- p.76 / References --- p.78

For the prediction of chaotic time series, a dichotomy has arisen between local approaches and global approaches. Local approaches hold the reputation of simplicity and feasibility, but they generally do not produce a compact description of the underlying system and are computationally intensive. Global approaches have the advantage of requiring less computation and are able to yield a global representation of the studied time series. However, due to the complexity of the time series process, it is often not easy to construct a global model to perform the prediction precisely. In addition to these approaches, a combination of the global and local techniques, called mixture of experts (ME), is also possible, where a smaller number of models work cooperatively to implement the prediction. This thesis reports on research about ME models for chaotic time series prediction. Based on a review of the techniques in time series prediction, a HMM-based ME model called "Time-line" Hidden Markov Experts (THME) is developed, where the trajectory of the time series is divided into some regimes in the state space and regression models called local experts are applied to learn the mapping on the regimes separately. The dynamics for the expert combination is a HMM, however, the transition probabilities are designed to be time-varying and conditional on the "real time" information of the time series. For the learning of the "time-line" HMM, a modified Baum-Welch algorithm is developed and the convergence of the algorithm is proved. Different versions of the model, based on MLP, RBF and SVM experts, are constructed and applied to a number of chaotic time series on both one-step-ahead and multi-step-ahead predictions. Experiments show that in general THME achieves better generalization performance than the corresponding single models in one-step-ahead prediction and comparable to some published benchmarks in multi-step-ahead prediction. Various properties of THME, such as the feature selection for trajectory dividing, the clustering techniques for regime extraction, the "time-line" HMM for expert combination and the performance of the model when it has different number of experts, are investigated. A number of interesting future directions for this work are suggested, which include the feature selection for regime extraction, the model selection for transition probability modelling, the extension to distribution prediction and the application on other time series.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Ohio State University, 1982. / Includes vita. Includes bibliographical references (leaves 125-133). Available online via OhioLINK's ETD Center.

There is a strong desire among automobile manufacturers to reduce the fuel consumption and greenhouse gas emissions of their current vehicles. Reducing the overall weight of a vehicle represents the most practical opportunity to reduce fuel consumption. Replacing the current steel sheet structures with lightweight alternatives, such as aluminum, offers an excellent solution. Much of the attention in North America has been focused on copper-containing Al-Mg-Si aluminum alloys (6xxx series), such as AA6111. These alloys offer an excellent combination of good formability and precipitation-strengthening ability.<p> In this study, the effect of solute concentration on the strength and strain aging behavior of a proprietary Al-Mg-Si-(Cu) alloy was evaluated. The experimental design used was a 26 full factorial design, with the primary factors being the solute concentrations of magnesium, silicon, and copper, as well as the effects of applied strain (cold work), and natural and artificial aging heat treatments (e.g., a simulated paint bake process). The primary investigative techniques employed included tensile testing, microhardness measurements, and optical metallography.<p> The results show that cold work and artificial aging produce the most substantial strengthening in the alloys. The occurrence of natural aging prior to forming and artificial aging reduced strengthening. The highest strength levels in the naturally aged and paint baked condition, which most closely resembles what is found in industry, were achieved at a combination of low magnesium levels (i.e., 0.5 wt.%) and high silicon and copper levels (i.e., 0.9 and 0.3 wt.%, respectively).

This thesis develops a novel robust periodogram method for detecting long memory. Though many test for long memory are based on the idea of linear regression, there exists no results in statistical literature on utilizing the robust regression methodology for detection of long memory. The advantage of the robust regression is a substantially less sensitivity to atypical observations or outliers, compared to the classical regression that is based on the least squares method. The thesis suggests two versions of the robust periodogram methods based on the least quan- tile and the least trimmed methods. The new robust periodogram methods are shown to provide smaller bias in long memory estimation when compared with the classical periodogram method. However, variability of estimation is increased. Therefore, we develop the bootstrapped modification of the new robust periodogram methods to reduce variability of estimation. The new bootstrapped modi¯cations of the robust periodogram tests substantially reduce variance of estimation and provides a competitively low bias. All proposed robust methods are illustrated by simulations and the case studies on currency exchange rates, and comparative analysis with other existing tests for long memory is carried out.

This thesis develops a novel robust periodogram method for detecting long memory. Though many test for long memory are based on the idea of linear regression, there exists no results in statistical literature on utilizing the robust regression methodology for detection of long memory. The advantage of the robust regression is a substantially less sensitivity to atypical observations or outliers, compared to the classical regression that is based on the least squares method. The thesis suggests two versions of the robust periodogram methods based on the least quan- tile and the least trimmed methods. The new robust periodogram methods are shown to provide smaller bias in long memory estimation when compared with the classical periodogram method. However, variability of estimation is increased. Therefore, we develop the bootstrapped modification of the new robust periodogram methods to reduce variability of estimation. The new bootstrapped modi¯cations of the robust periodogram tests substantially reduce variance of estimation and provides a competitively low bias. All proposed robust methods are illustrated by simulations and the case studies on currency exchange rates, and comparative analysis with other existing tests for long memory is carried out.

There is a strong desire among automobile manufacturers to reduce the fuel consumption and greenhouse gas emissions of their current vehicles. Reducing the overall weight of a vehicle represents the most practical opportunity to reduce fuel consumption. Replacing the current steel sheet structures with lightweight alternatives, such as aluminum, offers an excellent solution. Much of the attention in North America has been focused on copper-containing Al-Mg-Si aluminum alloys (6xxx series), such as AA6111. These alloys offer an excellent combination of good formability and precipitation-strengthening ability.<p> In this study, the effect of solute concentration on the strength and strain aging behavior of a proprietary Al-Mg-Si-(Cu) alloy was evaluated. The experimental design used was a 26 full factorial design, with the primary factors being the solute concentrations of magnesium, silicon, and copper, as well as the effects of applied strain (cold work), and natural and artificial aging heat treatments (e.g., a simulated paint bake process). The primary investigative techniques employed included tensile testing, microhardness measurements, and optical metallography.<p> The results show that cold work and artificial aging produce the most substantial strengthening in the alloys. The occurrence of natural aging prior to forming and artificial aging reduced strengthening. The highest strength levels in the naturally aged and paint baked condition, which most closely resembles what is found in industry, were achieved at a combination of low magnesium levels (i.e., 0.5 wt.%) and high silicon and copper levels (i.e., 0.9 and 0.3 wt.%, respectively).