Moderate deviations for the mildly stationary autoregressive models with dependent errors

In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive model with dependent errors which are modeled as a mildly stationary AR(1) process. By martingale methods, we establish the moderate deviations for the least squares estimators of the regressor and error, which can be applied to understand the near-integrated second order autoregressive processes. As an application, we obtain the moderate deviations for the Durbin-Watson statistic.Comment: Comments welcome. 28 page

A Study of Cross-Cultural Decision-Making Styles: Is Cognitive Mapping an Effective Methodology for Data Analysis?

In an increasingly globalised business environment, the behaviour and performance of managers from one country and culture working in another has never been more relevant. Of particular interest is the behaviour of expatriate managers from the west, working in Asian-owned or joint-venture companies situated in East Asia, and making decisions individually and as a group. The current study differs from many that are based around questionnaires and a quantitative methodology. It is a qualitative investigation into expatriate French managers based in China, and working in Chinese and French-Chinese companies. The choice of French managers situated in China is based on the distinct differences in culture, and the author’s own background as a Chinese manager working with French colleagues. The familiarity of the author with both the Chinese and French business cultures is important for useful interpretation of the data. The understanding of both cultures used in creating the methodology, interviewing, analysing and interpreting the data is an important and distinctive aspect of the research. The research looks at the manager's choice of individual or group decision-making styles, and the factors that influence the manager's choices in different cultural contexts. It uses cognitive mapping techniques to analyse 20 interview transcripts. The study concluded that cognitive mapping is a viable methodology to gain insights into the manager's individual and group decision-making styles. This methodology provided an opportunity to follow the manager's thinking around the choices they made, and to identify the factors that influence the manager's decision-making styles and process

DEVELOPMENT OF MAP/REDUCE BASED MICROARRAY ANALYSIS TOOLS

High density oligonucleotide array (microarray) from the Affymetrix GeneChip¨ system has been widely used for the measurements of gene expressions. Currently, public data repositories, such as Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), have accumulated very large amount of microarray data. For example, there are 84389 human and 9654 Arabidopsis microarray experiments in GEO database. Efficiently integrative analysis large amount of microarray data will provide more knowledge about the biological systems. Traditional microarray analysis tools all implemented sequential algorithms and can only be run on single processor. They are not able to handle very large microarray data sets with thousands of experiments. It is necessary to develop new microarray analysis tools using parallel framework. In this thesis, I implemented microarray quality assessment, background correction, normalization and summarization algorithms using the Map/Reduce framework. The Map/Reduce framework, first introduced by Google in 2004, offers a promising paradigm to develop scalable parallel applications for large-scale data. Evaluation of our new implementation on large microarray data of rice and Arabidopsis showed that they have good speedups. For example, running rice microarray data using our implementations of MAS5.0 algorithms on 20 computer nodes totally 320 processors has a 28 times speedup over using previous C++ implementation on single processor. Our new microarray tools will make it possible to utilize the valuable experiments in the public repositories