Wing-Yeong Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 73-75). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The observed data --- p.3 / Chapter 1.3 --- Outline of the thesis --- p.8 / Chapter Chapter 2 --- Modeling using Latent Variable --- p.9 / Chapter Chapter 3 --- Imputation Procedure --- p.16 / Chapter 3.1 --- Introduction --- p.16 / Chapter 3.2 --- Introduction to Metropolis-Hastings algorithm --- p.18 / Chapter 3.3 --- Introduction to Gibbs sampler --- p.19 / Chapter 3.4 --- Imputation step --- p.21 / Chapter 3.5 --- Initialization of the missing values by regression --- p.23 / Chapter 3.6 --- Initialization of the parameters and creating the latent variable and noises --- p.27 / Chapter 3.7 --- Simulation of Y's --- p.30 / Chapter 3.8 --- Simulation of the parameters --- p.34 / Chapter 3.9 --- Simulation of T by use of the Metropolis-Hastings algorithm --- p.41 / Chapter 3.10 --- Distribution of Vij's given all other values --- p.44 / Chapter 3.11 --- Simulation procedure of Vij's --- p.46 / Chapter Chapter 4 --- Data Analysis of the Pollutant Data --- p.48 / Chapter 4.1 --- Convergence of the process --- p.48 / Chapter 4.2 --- Data analysis --- p.53 / Chapter Chapter 5 --- Conclusion --- p.69 / REFERENCES --- p.73

潜变量模型作为处理多元数据的一种有效的方法，在行为学、教育学、社会心理学以及医学等各个领域都受到了广泛关注。在分析潜变量模型时，大多数现有的统计方法和软件都是基于响应变量为正态分布的假设。尽管一些最近发展的方法可以处理部分的非正态数据，但在分析高度非正态的数据时依然存在问题。此外，在实际研究中还经常会遇到不完全数据，如缺失数据和删失数据。简单地忽略或错误地处理不完全数据可能会严重扭曲统计结果。在本文中，我们发展了贝叶斯惩罚样条方法，同时采用马尔科夫链蒙特卡洛方法，用以分析存有高度非正态和不完全数据的变换潜变量模型。我们在变换潜变量模型中讨论了不同类型的不完全数据，如完全随机缺失数据、随机缺失数据、不可忽略的缺失数据以及删失数据。我们还利用离差信息准则来选择正确的模型和数据缺失机制。我们通过许多模拟研究论证了我们提出的方法。此方法被应用于关于工作满意度、家庭生活、工作态度的研究，以及香港地区2 型糖尿病患者心血管疾病的研究。 / Latent variable models (LVMs), as useful multivariate techniques, have attracted significant attention from various fields, including the behavioral, educational, social-psychological, and medical sciences. In the analysis of LVMs, most existing statistical methods and software have been developed under the normal assumption of response variables. While some recent developments can partially address the non-normality of data, they are still problematic in dealing with highly non-normal data. Moreover, the presence of incomplete data, such as missing data and censoring data, is a practical issue in substantive research. Simply ignoring incomplete data or wrongly managing incomplete data might seriously distort statistical influence results. In this thesis, we develop a Bayesian P-spline approach, coupled with Markov chain Monte Carlo (MCMC) methods, to analyze transformation LVMs with highly non-normal and incomplete data. Different types of incomplete data, such as missing completely at random data, missing at random data, nonignorable missing data, as well as censored data, are discussed in the context of transformation LVMs. The deviance information criterion is proposed to conduct model comparison and select an appropriate missing mechanism. The empirical performance of the proposed methodologies is examined via many simulation studies. Applications to a study concerning people's job satisfaction, home life, and work attitude, as well as a study on cardiovascular diseases for type 2 diabetic patients in Hong Kong are presented. / Detailed summary in vernacular field only. / Liu, Pengfei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 115-127). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Latent Variable Models --- p.1 / Chapter 1.2 --- Missing Data --- p.4 / Chapter 1.3 --- Censoring Data --- p.5 / Chapter 1.4 --- Penalized B-splines --- p.6 / Chapter 1.5 --- Bayesian Methods --- p.7 / Chapter 1.6 --- Outline of the Thesis --- p.8 / Chapter 2 --- Transformation Structural Equation Models --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.2 --- Model Description --- p.11 / Chapter 2.3 --- Bayesian Estimation --- p.12 / Chapter 2.3.1 --- Bayesian P-splines --- p.12 / Chapter 2.3.2 --- Identifiability Constraints --- p.15 / Chapter 2.3.3 --- Prior Distributions --- p.16 / Chapter 2.3.4 --- Posterior Inference --- p.18 / Chapter 2.4 --- Bayesian Model Selection via DIC --- p.20 / Chapter 2.5 --- Simulation Studies --- p.23 / Chapter 2.5.1 --- Simulation 1 --- p.23 / Chapter 2.5.2 --- Simulation 2 --- p.26 / Chapter 2.5.3 --- Simulation 3 --- p.27 / Chapter 2.6 --- Conclusion --- p.28 / Chapter 3 --- Transformation SEMs with Missing Data that are Missing At Random --- p.43 / Chapter 3.1 --- Introduction --- p.43 / Chapter 3.2 --- Model Description --- p.45 / Chapter 3.3 --- Bayesian Estimation and Model Selection --- p.46 / Chapter 3.3.1 --- Modeling Transformation Functions --- p.46 / Chapter 3.3.2 --- Identifiability Constraints --- p.47 / Chapter 3.3.3 --- Prior Distributions --- p.48 / Chapter 3.3.4 --- Bayesian Estimation --- p.49 / Chapter 3.3.5 --- Model Selection via DIC --- p.52 / Chapter 3.4 --- Simulation Studies --- p.53 / Chapter 3.4.1 --- Simulation 1 --- p.54 / Chapter 3.4.2 --- Simulation 2 --- p.56 / Chapter 3.5 --- Conclusion --- p.57 / Chapter 4 --- Transformation SEMs with Nonignorable Missing Data --- p.65 / Chapter 4.1 --- Introduction --- p.65 / Chapter 4.2 --- Model Description --- p.67 / Chapter 4.3 --- Bayesian Inference --- p.68 / Chapter 4.3.1 --- Model Identification and Prior Distributions --- p.68 / Chapter 4.3.2 --- Posterior Inference --- p.69 / Chapter 4.4 --- Selection of Missing Mechanisms --- p.71 / Chapter 4.5 --- Simulation studies --- p.73 / Chapter 4.5.1 --- Simulation 1 --- p.73 / Chapter 4.5.2 --- Simulation 2 --- p.76 / Chapter 4.6 --- A Real Example --- p.77 / Chapter 4.7 --- Conclusion --- p.79 / Chapter 5 --- Transformation Latent Variable Models with Multivariate Censored Data --- p.86 / Chapter 5.1 --- Introduction --- p.86 / Chapter 5.2 --- Model Description --- p.88 / Chapter 5.3 --- Bayesian Inference --- p.90 / Chapter 5.3.1 --- Model Identification and Bayesian P-splines --- p.90 / Chapter 5.3.2 --- Prior Distributions --- p.91 / Chapter 5.3.3 --- Posterior Inference --- p.93 / Chapter 5.4 --- Simulation Studies --- p.96 / Chapter 5.4.1 --- Simulation 1 --- p.96 / Chapter 5.4.2 --- Simulation 2 --- p.99 / Chapter 5.5 --- A Real Example --- p.100 / Chapter 5.6 --- Conclusion --- p.103 / Chapter 6 --- Conclusion and Further Development --- p.113 / Bibliography --- p.115

Video games are played by more than 1.8 billion people and are a pervasive force in society, but despite decades of research there has been little consensus on their effects. Before we are able to model complex outcomes such as excessive engagement, we must first understand how and why people play video games. This dissertation integrates latent factor models with techniques from machine learning and network analysis to develop a holistic picture of gaming style, motivations, and individual differences. It employs diverse sources of data across several studies and a total of 2,143 participants, combining online questionnaires with qualitative analysis of participant responses and objective information about gaming behaviour from the API of the popular gaming network "Steam", and finds that stress relief is a primary motivation for engaging in the immersive worlds of video games. Previous research has indicated three underlying factors of Immersion, Achievement and Socialising which replicated across three comprehensive studies of 480 adults, 106 adults and children with an Autism Spectrum Condition, and 961 adults and adolescents. Gamers experiencing more stress in their daily lives were more likely to have Immersion rather than Social or Achievement play styles. Achievement-oriented gamers tended to be lower in stress, higher in conscientiousness and emotional stability, and played more than Immersion-focused gamers. A qualitative analysis of 54 gamers' descriptions of why they recently chose to play a game was used to develop the "Reasons for Playing Video Games" items (RPVG), which were administered to independent samples of 243, 299 and 961 gamers. The qgraph R package was used to perform network analyses of the RPVG items and gameplay style factors, employing the machine learning-based adaptive LASSO technique to estimate a partial correlation matrix from a set of variables as a Pairwise Markov Random Field. Gamers higher in Immersion tended to play for escapism, distraction, and fantasy, while social gamers played for excitement, energy, and self-expression. Network analysis and graph theory illustrate the central role of stress relief in the network of Reasons for Playing Video Games and shows that playing when feeling stressed is strongly linked with Immersion.

The dissertation includes three papers that address some theoretical and technical issues of latent variable models. The first paper extends the uniformly most powerful test approach for testing person parameter in IRT to the two-parameter logistic models. In addition, an efficient branch-and-bound algorithm for computing the exact p-value is proposed. The second paper proposes a reparameterization of the log-linear CDM model. A Gibbs sampler is developed for posterior computation. The third paper proposes an ordered latent class model with infinite classes using a stochastic process prior. Furthermore, a nonparametric IRT application is also discussed.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 139-145).

We present a mixture of latent trait models with common slope parameters (MCLT) for high dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a d-dimensional Gaussian latent variable, is extended by implementing common factor analyzers. We extend the model further by the incorporation of random block effects. The dependencies in each block are taken into account through block-specific parameters that are considered to be random variables. A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters. The Bayesian information criterion is used to select the number of components and the covariance structure as well as the dimensions of latent variables. Our approach is demonstrated on U.S. Congressional voting data and on a data set describing the sensory properties of orange juice. Our examples show that our model performs well even when the number of observations is not very large relative to the data dimensionality. In both cases, our approach yields intuitive clustering results. Additionally, our dimensionality-reduction method allows data to be displayed in low-dimensional plots. / Early Researcher Award from the Government of Ontario (McNicholas); NSERC Discovery Grants (Browne and McNicholas).

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 168-180).

Thesis (Ph. D.)--Carleton University, 2004. / Includes bibliographical references (p. 255-269). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Michigan State University. Dept. of Counseling, Educational Psychology and Special Education, 2008. / Title from PDF t.p. (viewed on July 23, 2009) Includes bibliographical references (p. 142-148). Also issued in print.

Thesis (Ph. D.)--University of Hong Kong, 1998. / Includes bibliographical references.