Variational inference for heteroscedastic and longitudinal regression models

University of Technology Sydney. Faculty of Science.The focus of this thesis is on the development and assessment of mean field variational Bayes (MFVB), which is a fast, deterministic tool for inference in a Bayesian hierarchical model setting. We assess the performance of MFVB via the use of comprehensive comparisons against a Markov chain Monte Carlo (MCMC) benchmark. Each of the models considered are special cases of semiparametric regression. In particular, we focus on the development and assessment of the performance of MFVB for heteroscedastic and longitudinal semiparametric regression models. Generally, the new MFVB methodology performs well in its assessment of accuracy against MCMC for the semiparametric and nonparametric regression models considered in this thesis. It is also much faster and is shown to be applicable to real-time analyses. Several real data illustrations are provided. Altogether, MFVB proves to be a credible inference tool and a good alternative to MCMC, especially when analysis is hindered by time constraints

Assessing the validity of brand equity constructs: A comparison of two approaches

This paper tests both the internal and external validity of the Erdem and Swait (1998) brand equity framework using two measurement modelling approaches, namely the relatively new Best-Worst Scaling (BWS) method (Finn and Louviere, 1992; Marley and Louviere, 2005) and the more traditional Confirmatory Factor Analysis (CFA) method. Data were collected from the Australian banking services sector. We find the measurement models derived from BWS outperformed the models based on CFA of the rating data in predicting both stated and real brand choices. The findings have implications for both academics and practitioners in brand equity measurement and management

Testing the Erdem and Swait Brand Equity Framework Using Latent Class Structural Equation Modelling

This paper tests the Erdem and Swait (1998) brand equity framework using latent class structural equation modelling. While there are a number of conceptual and measurement models of brand equity in the literature, we focus on the Erdem and Swait brand equity framework because it is based on formal theory in information economics. The Erdem and Swait framework was originally tested in a structural equation modelling framework without taking into account consumer preference heterogeneity. In this study, we extend the Erdem and Swait framework to incorporate preference heterogeneity via the use of latent class structural equation modelling. Data were collected from the financial services sector and results show two distinct segments of brand equity. The findings have implications for both academics and practitioners in brand management

Assessing the Acquiescence Bias of Online Research Data

The impact of acquiescence bias in online samples is real and deserves serious research attention. This paper assesses the impact of acquiescence bias of online respondents on research output. Specifically, this paper addresses one type of acquiescence bias being increasingly observed in online panel rating scale data, where respondents exhibit low variability across rating scale items. This type of acquiescence bias is defined as flat line response bias in this study. The insidious effects of flat line response bias will be demonstrated on market segmentation and structural equation modelling in the context of a brand equity framework. This paper urges the market research industry to improve online recruitment and management to reduce flat line response bias in online panel surveys

Real-time Semiparametric Regression via Sequential Monte Carlo

We develop and describe online algorithms for performing real-time semiparametric regression analyses. Earlier work on this topic is in Luts, Broderick & Wand (J. Comput. Graph. Statist., 2014) where online mean field variational Bayes was employed. In this article we instead develop sequential Monte Carlo approaches to circumvent well-known inaccuracies inherent in variational approaches. Even though sequential Monte Carlo is not as fast as online mean field variational Bayes, it can be a viable alternative for applications where the data rate is not overly high. For Gaussian response semiparametric regression models our new algorithms share the online mean field variational Bayes property of only requiring updating and storage of sufficient statistics quantities of streaming data. In the non-Gaussian case accurate real-time semiparametric regression requires the full data to be kept in storage. The new algorithms allow for new options concerning accuracy/speed trade-offs for real-time semiparametric regression