This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models. I use Autoregressive Integrated Moving Average (ARIMA) intervention models and the Pierce and Haugh statistical test to model the impact of a single marketing instrument, mainly price promotions, to measure own and cross-short term sales effects, and to study asymmetric marketing competition. I develop and apply Vector AutoRegressive (VAR) and Bayesian Vector AutoRegressive (BVAR) models to estimate dynamic relationships in the market and to forecast market share. Especially, BVAR models are advantageous because they contain all relevant dynamic and interactive effects. They accommodate not only classical competitive reaction effects, but also own and cross-market share brand feedback effects and internal decision rules and provided substantively useful insights into the dynamics of demand. The integrative essay is structured in four main parts. The introduction sets the basic ideas behind the published papers, with particular focus on the motivation of the essay, the types of competitive reaction effects analysed, an overview of the time series econometric models in marketing, a short discussion of the basic methodology used in the research and a brief description of the inter-relationships across the published papers and structure of the essay. The discussion is centred on how to model the effects of marketing actions at the selective demand or brand level and at the primary demand or product level. At the brand level I discuss the research contribution of my work on (i) modelling promotional short-term effects of price and non-price actions on sales and market share for consumer packaged goods, with no competition, (ii) how to measure own and cross short-term sales effects of advertising and price, in particular, cross-lead and lag effects, asymmetric sales behaviour and competition without retaliatory actions, in an automobile market, (iii) how to model the marketing-mix effectiveness at the short and long-term on market shares in a car market, (iv) what is the best method to forecast market share, and (v) the study of causal linkages at different time horizons between sales and marketing activity for a particular brand. At the product or commodity level, I propose a way to model the flows of tourists that come from different origins (countries) to the same country-destination as market segments defining the primary demand of a commodity - the product

Josephson junction qubits are promising candidates for a scalable quantum processor. Such qubits are commonly manipulated by means of sequences of rf-pulses and different methods are used to determine their quantum state. The readout should be able to distinguish the two qubit states with high accuracy and be faster than the relaxation time of the qubit. We discuss and experiment with a readout method based on the switching of a Josephson junction from the zero voltage state to a finite voltage state. The Josephson junction circuit has a non-linear dynamics and when it is brought to a bifurcation point, it can be made arbitrarily sensitive to small perturbations. This extreme sensitivity at a bifurcation point can be used to distinguish the two quantum states if the topology of the phase space of the circuit leads to a quick separation into the final states where re-crossings of the bifurcation point are negligible. We optimize a switching current detector by analyzing the phase space of a Josephson junction circuit with frequency dependent damping. A pulse and hold technique is used where an initial current pulse brings the junction close to its bifurcation point and the subsequent hold level is used to give the circuit enough time to evolve until the two states can be distinguished by the measuring instrument. We generate the pulse and hold waveform by a new technique where a voltage step with following linear voltage rise is applied to a bias capacitor. The frequency dependent damping is realized by an on-chip RC-environment fabricated with optical lithography. Josephson junction circuits are added on by means of e-beam lithography. Measurements show that switching currents can be detected with pulses as short as 5 ns and a resolution of 2.5% for a sample directly connected to the measurement leads of the cryostat. Detailed analysis of the switching currents in the RC-environment show that pulses with a duration of 20 us can be explained by a generalization of Kramers' escape theory, whereas switching the same sample with 25 ns pulses occurs out of thermal equilibrium, with sensitivity and speed adequate for qubit readout. / QC 20100924

Spelar finansiering roll och hur bör fördelningen mellan eget kapital och skulder se ut? Kapitalstruktur har studerats i många års tid och forskare har försökt finna de faktorer som kan påverka kapitalstruktur och om skuldsättningsgraden påvisar samband mellan olika betydande variabler. Syftet med denna studie är att undersöka kapitalstrukturen hos företag inom olika branscher i avseende på skuldsättningsgrad, tillväxt, företagsstorlek och företagsålder. Undersökningen är genomförd utifrån en kvantitativ ansats som omfattar 50 företag på NASDAQ OMX Stockholm. Korrelation- samt regressionsanalyser genomfördes för att undersöka sambandet mellan företagsstorlek, företagsålder samt tillväxttakt som oberoende variabler och skuldsättningsgrad som beroende variabel. Studien har visat att företagsålder är en branschspecifik variabel som påverkar skuldsättningsgraden på olika sätt beroende på bransch. Samtidigt som företagsstorlek och tillväxttakt har visat sig vara icke branschspecifika variabler. Studien har även kunnat klargöra att vissa branscher är mer homogena än andra i avseende på kapitalstruktur. / How important is the capital structure and how should equity and debt be divided within a company? Capital structure has been studied for many years, and researchers have attempted to identify the factors that can affect the capital structure and if the leverage ratio shows significant correlations between different variables. The purpose of this study is to examine the capital structure of companies in different industries in terms of leverage, growth, company size and company age. The survey was conducted based on a quantitative approach, which includes 50 companies on NASDAQ OMX Stockholm. Correlation-and regression analyzes were conducted to examine the relationship between company size, company age and growth rate as independent variables and leverage as the dependent variable.  The study has shown that business age is an industry-specific variable affecting leverage in different ways depending on the industry. While firm size and growth rate has been found to be non-industry-specific variables. The study was also able to clarify that some industries aremore homogenous than others in terms of capital structure.

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works. This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution. Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard. A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

The objective of this master thesis is to identify “key-drivers” embedded in customer satisfaction data. The data was collected by a large transportation sector corporation during five years and in four different countries. The questionnaire involved several different sections of questions and ranged from demographical information to satisfaction attributes with the vehicle, dealer and several problem areas. Various regression, correlation and cooperative game theory approaches were used to identify the key satisfiers and dissatisfiers. The theoretical and practical advantages of using the Shapley value, Canonical Correlation Analysis and Hierarchical Logistic Regression has been demonstrated and applied to market research.

A simple correlation analysis was used to investigate the linear relationships between sea surface temperature (SST) and monthly flow of Ganges and Brahmaputra at the borders of Bangladesh and India using approximately 50 years of river discharge data. Strong correlations were found between the equatorial Pacific SST and boreal summer Ganges discharge from three-month lag to two-month lead times. The El Nio-Southern Oscillation (ENSO) explains Ganges flow variance exceeding 0.95 significance level using both the Nino 3.4 SST correlation and the composites made for El Nio (La Nina) periods. The May SST of the southwest Pacific Ocean to the east of Australia continent has a strong correlation (>0.6) with early summer Ganges discharges. Using a lag correlation analysis of Ganges discharge and SST, we found a steady and continuous development in the Nino 3.4 SST relationship, and a strong correlation with the southwest Pacific SST which is most pronounced three-four months prior to the onset of Asian summer monsoon. These relationships mean that at least 25% of the interannual summer Ganges River discharge variability can be explained by antecedent equatorial and southwest Pacific SST. It provides a possible statistical method for linear forecasting two or three months in advance. The Brahmaputra River discharge, on the other hand, shows weak relationships with tropical SST variability except for the Bay of Bengal and the higher northern latitudes of the Pacific.

In response to organic contaminations pollutating water sources of drinking water, domestic water treatment plants (WTP) were transforming from traditional chlorination disinfecton method to advanced ozone-based disinfection processes. However, the effectiveness of water purification procedures n removing AOC (Assimilable Organic Carbon) and DBPsFP (Disinfection By-Products Formation Potential) can be improved. Additionally, the quality of clean water purified at WTP may deteriorate in the water distribution network for various reasons, primarily resulting from the regrowth of microorganisms in the water distribution pipelines. This study investigates and researches the essential water quality items of effluent before and after the advanced water purification treatment plants and water movement to end users through water distribution networks. The investigation proceeded in four directions: (1) the efficiency of removing AOC from raw water using powdered and granular activated carbon biological systems, and the development of an AOC prediction model based on water quality monitoring items using the AutoNet (6.03) method of the artificial neural network system; (2) removal of the byproducts of disinfection from raw water using powdered activated carbon biological systems; (3) examining the relationship between ozone-based and chlorination-based water disinfection methods by comparing the number of coliform bacteria and total bacteria population in traditional and advanced processing units; (4) regarding the water distribution storage facilities for users, water reservoir towers were examined for water quality sampling and analysis and water tower cleaning frequencies. Regression analysis was performed using SPSS ¡]Statistical Product and Service Solutions¡^ statistical software, with the correlation coefficient denoting the closeness of relationships. We anticipate understanding the water quality situation for current users of tap water, and demands for cleaning frequencies, thereby achieving the purpose of improving drinking water safety. Regarding the efficiency of removing AOC from raw water, the results showed powdered and granular activated carbon biological systems performed well, with the AOC removal rate reaching 53% and 54%, respectively, and the SUVA (Specific Ultraviolet Absorbance) value (showed by UV254/DOC) being reduced by 15-18% and 22-23%, respectively. The correlation analysis of the AOC prediction model shows that the GAC (Granular Activated Carbon) had high predictive and actual value R values (R2 = 0.772) after model regressing, and the PAC (Powder Activated Carbon) had higher predictive and actual value R values (R2 = 0.856) after model regressing as well. That the PAC system AOC prediction model has a slightly higher correlation that may be attributed to water contaminations resulting from domestic sewage, agricultural fertilizers, and livestock excretions. In the use of powdered activated carbon biological systems to remove disinfection byproducts, THMsFP (Trihalomethanes Formation Potential) and HAAsFP (Haloacetic acids Formation Potential) functioned with a certain removal efficiency, with the average effluent concentrations being under the regulatory standard of 80£gg/L, respectively, which reduces carcinogenic risks. Correlation analyses conducted using SUVA on the three water quality concentrations (HAA5FP, HAA9FP, and THMsFP) obtained R2 values of 0.805, 0.820, and 0.823, respectively, indicating high levels of correlation. For the results of microbial assessment using ozone and chlorine to process drinking water, the advanced and conventional WTP achieved a removal rate greater than 99% for microbial removal (coliform bacteria and total bacteria population). The correlation analysis between cleaning frequencies and water quality parameters showed the frequency at which the water reservoirs and towers were cleaned has a significant impact on tap water quality in residential compounds and schools that accommodated more than 100 households or less than 99 households. Higher cleaning frequency (more than four cleanings a year) results in better the water quality.

The topic of National Innovation System (NIS) is gradually emphasized. The NIS includes four compositions. They are government, industry, university and public research organization. The knowledge flow is transmitted among the four compositions through innovation policy. Thus, many countries have begun to develop NIS. The NIS will raise the economic growth rate, and promote the competitiveness of industry. Consequently, the study of NIS becomes very popular. OECD (Organization for Economic Cooperation and Development) build particular NIS structures to explain the difference between members, and try to find the key successful way to achieve national innovative goals. In the Asia, the Taiwan, China, Japan and South Korea show high relationship in the politics and economics. Japan and South are high-developing countries, and their innovation activities are very successful in the world, especially in those of technology industry. Additionally, China has abundant natural resources to help them develop technology industries. For above reasons, we elect these countries to be studied, and we try to find the essential factors of successful NIS. This study includes two research issues. We first collect the secondary data to explain different NIS structure among four countries. Then, we use Stepwise Regression Analysis to evaluate the performance of innovation. Finally we use the Pearson Correlation Analysis to analyze relationship between NIS performance and semiconductor industry development. The results of this study include: (1) R&D expenditure is the most important factor to influence the performance of national innovation; (2) Expenditure on basic research is an important factor to influence the output of innovation; (3) national innovation and industry development shows high relationship; and (4) the ranking of national innovation performance is not totally the same as that of industry development. Based on these findings, we will provide some important policy suggestions for innovation activities in Taiwan.

Modern data collection methods are now frequently returning observations that should be viewed as the result of digitized recording or sampling from stochastic processes rather than vectors of finite length. In spite of great demands, only a few classification methodologies for such data have been suggested and supporting theory is quite limited. The focus of this dissertation is on discrimination and classification in this infinite dimensional setting. The methodology and theory we develop are based on the abstract canonical correlation concept of Eubank and Hsing (2005), and motivated by the fact that Fisher's discriminant analysis method is intimately tied to canonical correlation analysis. Specifically, we have developed a theoretical framework for discrimination and classification of sample paths from stochastic processes through use of the Loeve-Parzen isomorphism that connects a second order process to the reproducing kernel Hilbert space generated by its covariance kernel. This approach provides a seamless transition between the finite and infinite dimensional settings and lends itself well to computation via smoothing and regularization. In addition, we have developed a new computational procedure and illustrated it with simulated data and Canadian weather data.

No description available.