A study on model selection of binary and non-Gaussian factor analysis.

An, Yujia.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 71-76).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.1.1 --- Review on BFA --- p.2Chapter 1.1.2 --- Review on NFA --- p.3Chapter 1.1.3 --- Typical model selection criteria --- p.5Chapter 1.1.4 --- New model selection criterion and automatic model selection --- p.6Chapter 1.2 --- Our contributions --- p.7Chapter 1.3 --- Thesis outline --- p.8Chapter 2 --- Combination of B and BI architectures for BFA with automatic model selection --- p.10Chapter 2.1 --- Implementation of BFA using BYY harmony learning with au- tomatic model selection --- p.11Chapter 2.1.1 --- Basic issues of BFA --- p.11Chapter 2.1.2 --- B-architecture for BFA with automatic model selection . --- p.12Chapter 2.1.3 --- BI-architecture for BFA with automatic model selection . --- p.14Chapter 2.2 --- Local minima in B-architecture and BI-architecture --- p.16Chapter 2.2.1 --- Local minima in B-architecture --- p.16Chapter 2.2.2 --- One unstable result in BI-architecture --- p.21Chapter 2.3 --- Combination of B- and BI-architecture for BFA with automatic model selection --- p.23Chapter 2.3.1 --- Combine B-architecture and BI-architecture --- p.23Chapter 2.3.2 --- Limitations of BI-architecture --- p.24Chapter 2.4 --- Experiments --- p.25Chapter 2.4.1 --- Frequency of local minima occurring in B-architecture --- p.25Chapter 2.4.2 --- Performance comparison for several methods in B-architecture --- p.26Chapter 2.4.3 --- Comparison of local minima in B-architecture and BI- architecture --- p.26Chapter 2.4.4 --- Frequency of unstable cases occurring in BI-architecture --- p.27Chapter 2.4.5 --- Comparison of performance of three strategies --- p.27Chapter 2.4.6 --- Limitations of BI-architecture --- p.28Chapter 2.5 --- Summary --- p.29Chapter 3 --- A Comparative Investigation on Model Selection in Binary Factor Analysis --- p.31Chapter 3.1 --- Binary Factor Analysis and ML Learning --- p.32Chapter 3.2 --- Hidden Factors Number Determination --- p.33Chapter 3.2.1 --- Using Typical Model Selection Criteria --- p.33Chapter 3.2.2 --- Using BYY harmony Learning --- p.34Chapter 3.3 --- Empirical Comparative Studies --- p.36Chapter 3.3.1 --- Effects of Sample Size --- p.37Chapter 3.3.2 --- Effects of Data Dimension --- p.37Chapter 3.3.3 --- Effects of Noise Variance --- p.39Chapter 3.3.4 --- Effects of hidden factor number --- p.43Chapter 3.3.5 --- Computing Costs --- p.43Chapter 3.4 --- Summary --- p.46Chapter 4 --- A Comparative Investigation on Model Selection in Non-gaussian Factor Analysis --- p.47Chapter 4.1 --- Non-Gaussian Factor Analysis and ML Learning --- p.48Chapter 4.2 --- Hidden Factor Determination --- p.51Chapter 4.2.1 --- Using typical model selection criteria --- p.51Chapter 4.2.2 --- BYY harmony Learning --- p.52Chapter 4.3 --- Empirical Comparative Studies --- p.55Chapter 4.3.1 --- Effects of Sample Size on Model Selection Criteria --- p.56Chapter 4.3.2 --- Effects of Data Dimension on Model Selection Criteria --- p.60Chapter 4.3.3 --- Effects of Noise Variance on Model Selection Criteria --- p.64Chapter 4.3.4 --- Discussion on Computational Cost --- p.64Chapter 4.4 --- Summary --- p.68Chapter 5 --- Conclusions --- p.69Bibliography --- p.7

Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms

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Intelligent Financial Fraud Detection Practices: An Investigation

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Optimizing feature extraction in image analysis using experimented designs, a case study evaluating texture algorithms for describing appearance retention in carpets

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Selection bias in dynamically-measured super-massive black hole samples: consequences for pulsar timing arrays

Supermassive black hole -- host galaxy relations are key to the computation of the expected gravitational wave background (GWB) in the pulsar timing array (PTA) frequency band. It has been recently pointed out that standard relations adopted in GWB computations are in fact biased-high. We show that when this selection bias is taken into account, the expected GWB in the PTA band is a factor of about three smaller than previously estimated. Compared to other scaling relations recently published in the literature, the median amplitude of the signal at $f=1$yr$^{-1}$ drops from $1.3\times10^{-15}$ to $4\times10^{-16}$. Although this solves any potential tension between theoretical predictions and recent PTA limits without invoking other dynamical effects (such as stalling, eccentricity or strong coupling with the galactic environment), it also makes the GWB detection more challenging.Comment: 6 pages 4 figures, submitted to MNRAS letter

The Determinants of Intrafirm Trade: Evidence from French Firms

How well does the theory of the firm explain the choice between intrafirm and arms' length trade? This paper uses firm-level import data from France to look into this question. We find support for three key predictions of property-rights theories of the multinational firm. Intrafirm imports are more likely: (i) in capital- and skill-intensive firms; (ii) in highly productive firms; (iii) from countries with well-functioning judicial institutions. We further bridge previous aggregate findings with our investigation by decomposing intrafirm imports into an extensive and intensive margin. Doing so we uncover interesting patterns in the data that require further theoretical investigation.intrafirm trade, outsourcing, firm heterogeneity, incomplete contracts, internationalization strategies, quality of institutions, extensive margin, intensive margin

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

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