A Python package for Bayesian estimation using Markov Chain Monte Carlo

Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems that are faced in the Bayesian analysis of statistical problems. The implementation of MCMC algorithms is, however, code intensive and time consuming. We have developed a Python package, which is called PyMCMC, that aids in the construction of MCMC samplers and helps to substantially reduce the likelihood of coding error, as well as aid in the minimisation of repetitive code. PyMCMC contains classes for Gibbs, Metropolis Hastings, independent Metropolis Hastings, random walk Metropolis Hastings, orientational bias Monte Carlo and slice samplers as well as specific modules for common models such as a module for Bayesian regression analysis. PyMCMC is straightforward to optimise, taking advantage of the Python libraries Numpy and Scipy, as well as being readily extensible with C or Fortran.

Consumer misbehaviour during professional service encounters

To date, consumer behaviour research is still over-focused on the functional rather than the dysfunctional. Both empirical and anecdotal evidence suggest that service organisations are burdened with the concept of consumer sovereignty, while consumers freely flout the ‘rules’ of social exchange and behave in deviant and dysfunctional ways. Further, the current scope of consumer misbehaviour research suggests that the phenomenon has principally been studied in the context of economically-focused exchange. This limits our current understanding of consumer misbehaviour to service encounters that are more transactional than relational in nature. Consequently, this thesis takes a Social Exchange approach to consumer misbehaviour and reports a three-stage multi-method study that examined the nature and antecedents of consumer misbehaviour in professional services. It addresses the following broad research question: What is the nature of consumer misbehaviour during professional service encounters? Study One initially explored the nature of consumer misbehaviour in professional service encounters using critical incident technique (CIT) within 38 semi-structured in-depth interviews. The study was designed to develop a better understanding of what constitutes consumer misbehaviour from a service provider’s perspective. Once the nature of consumer misbehaviour had been qualified, Study Two focused on developing and refining calibrated items that formed Guttman-like scales for two consumer misbehaviour constructs: one for the most theoretically-central type of consumer misbehaviour identified in Study One (i.e. refusal to participate) and one for the most well-theorised and salient type of consumer misbehaviour (i.e. verbal abuse) identified in Study One to afford a comparison. This study used Rasch modelling to investigate whether it was possible to calibrate the escalating severity of a series of decontextualised behavioural descriptors in a valid and reliable manner. Creating scales of calibrated items that capture the variation in severity of different types of consumer misbehaviour identified in Study One allowed for a more valid and reliable investigation of the antecedents of such behaviour. Lastly, Study Three utilised an experimental design to investigate three key antecedents of consumer misbehaviour: (1) the perceived quality of the service encounter [drawn from Fullerton and Punj’s (1993) model of aberrant consumer behaviour], (2) the violation of consumers’ perceptions of justice and equity [drawn from Rousseau’s (1989) Psychological Contract Theory], and (3) consumers’ affective responses to exchange [drawn from Weiss and Cropanzano’s (1996) Affective Events Theory]. Investigating three key antecedents of consumer misbehaviour confirmed the newly-developed understanding of the nature of consumer misbehaviour during professional service encounters. Combined, the results of the three studies suggest that consumer misbehaviour is characteristically different within professional services. The most salient and theoretically-central behaviours can be measured using increasingly severe decontextualised behavioural descriptors. Further, increasingly severe forms of consumer misbehaviour are likely to occur as a response to consumer anger at low levels of interpersonal service quality. These findings have a range of key implications for both marketing theory and practice.

The impact of software-as-a-service on business models of leading software vendors : experiences from three exploratory case studies

The number of software vendors offering ‘Software-as-a-Service’ has been increasing in recent years. In the Software-as-a-Service model software is operated by the software vendor and delivered to the customer as a service. Existing business models and industry structures are challenged by the changes to the deployment and pricing model compared to traditional software. However, the full implications on the way companies create, deliver and capture value are not yet sufficiently analyzed. Current research is scattered on specific aspects, only a few studies provide a more holistic view of the impact from a business model perspective. For vendors it is, however, crucial to be aware of the potentially far reaching consequences of Software-as-a-Service. Therefore, a literature review and three exploratory case studies of leading software vendors are used to evaluate possible implications of Software-as-a-Service on business models. The results show an impact on all business model building blocks and highlight in particular the often less articulated impact on key activities, customer relationship and key partnerships for leading software vendors and show related challenges, for example, with regard to the integration of development and operations processes. The observed implications demonstrate the disruptive character of the concept and identify future research requirements.

Multicast traffic filtering for sampled value process bus networks

Ethernet is a key component of the standards used for digital process buses in transmission substations, namely IEC 61850 and IEEE Std 1588-2008 (PTPv2). These standards use multicast Ethernet frames that can be processed by more than one device. This presents some significant engineering challenges when implementing a sampled value process bus due to the large amount of network traffic. A system of network traffic segregation using a combination of Virtual LAN (VLAN) and multicast address filtering using managed Ethernet switches is presented. This includes VLAN prioritisation of traffic classes such as the IEC 61850 protocols GOOSE, MMS and sampled values (SV), and other protocols like PTPv2. Multicast address filtering is used to limit SV/GOOSE traffic to defined subsets of subscribers. A method to map substation plant reference designations to multicast address ranges is proposed that enables engineers to determine the type of traffic and location of the source by inspecting the destination address. This method and the proposed filtering strategy simplifies future changes to the prioritisation of network traffic, and is applicable to both process bus and station bus applications.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Fast rates for estimation error and oracle inequalities for model section

We consider complexity penalization methods for model selection. These methods aim to choose a model to optimally trade off estimation and approximation errors by minimizing the sum of an empirical risk term and a complexity penalty. It is well known that if we use a bound on the maximal deviation between empirical and true risks as a complexity penalty, then the risk of our choice is no more than the approximation error plus twice the complexity penalty. There are many cases, however, where complexity penalties like this give loose upper bounds on the estimation error. In particular, if we choose a function from a suitably simple convex function class with a strictly convex loss function, then the estimation error (the difference between the risk of the empirical risk minimizer and the minimal risk in the class) approaches zero at a faster rate than the maximal deviation between empirical and true risks. In this paper, we address the question of whether it is possible to design a complexity penalized model selection method for these situations. We show that, provided the sequence of models is ordered by inclusion, in these cases we can use tight upper bounds on estimation error as a complexity penalty. Surprisingly, this is the case even in situations when the difference between the empirical risk and true risk (and indeed the error of any estimate of the approximation error) decreases much more slowly than the complexity penalty. We give an oracle inequality showing that the resulting model selection method chooses a function with risk no more than the approximation error plus a constant times the complexity penalty.

PTZ camera pose estimation by tracking a 3D target

We present a technique for estimating the 6DOF pose of a PTZ camera by tracking a single moving target in the image with known 3D position. This is useful in situations where it is not practical to measure the camera pose directly. Our application domain is estimating the pose of a PTZ camerso so that it can be used for automated GPS-based tracking and filming of UAV flight trials. We present results which show the technique is able to localize a PTZ after a short vision-tracked flight, and that the estimated pose is sufficiently accurate for the PTZ to then actively track a UAV based on GPS position data.

Unbiased estimation of the half-life to price index convergence among U.S. cities

Estimates of the half-life to convergence of prices across a panel of cities are subject to bias from three potential sources: inappropriate cross-sectional aggregation of heterogeneous coefficients, presence of lagged dependent variables in a model with individual fixed effects, and time aggregation of commodity prices. This paper finds no evidence of heterogeneity bias in annual CPI data for 17 U.S. cities from 1918 to 2006, but correcting for the “Nickell bias” and time aggregation bias produces a half-life of 7.5 years, shorter than estimates from previous studies.