Dissertation

The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatment effects on a categorical response variable via a linear parameterization. The response may be polytomous, ordinal or dichotomous. The main tool is the log-linear representation of appropriately parameterized Rasch-type models, which can be fitted using standard software, e.g., R. The proposed method is applied to data from a psychiatric study on the evaluation of psychobiological variables in the therapy of depression. The effects of plasma levels of the antidepressant drug Clomipramine and neuroendocrinological variables on the presence or absence of anxiety symptoms in 45 female patients are analyzed. The individual measurements of the time dependent variables were recorded on 2 to 11 occasions. The findings show that certain combinations of the variables investigated are favorable for the treatment outcome. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

To achieve cooperative diversity in cognitive radio network, Decode and Forward (DF) protocol is implemented at Cognitive Radios (CRs) using Orthogonal Space Time Block Coding (OSTBC). The 2-hop communication between source and destination is completed with the help of Cognitive Relays (CRs) using Multiple Input Multiple Output (MIMO) technology within the network. To achieve spatial diversity and good code rate Alamouti 2×2 STBC is used for transmission. CR is using the decoding (Decode and Forward (DF)) strategy and without amplifying ability before forwarding data towards destination provide better performance. The main objective of this thesis is to detect Primary User (PU) spectrum availability or non-availability for the use of Secondary Users (SU). The Alamouti STBC encoded data is broadcasted to wireless Rayleigh faded channel through transmitter having two transmitting antennas. The CRs are preferred to place close with PU to detect transmitted signal and because of having decoding capability CRs decode the collected data using Maximum Likelihood (ML) decoding technique then re-encode the decoded data for further transmission towards receiver. The energy of PU signal received at relays is calculated using energy detector used at cognitive controller having authority to make final decision about presence or absence of PU signal within the spectrum by comparing calculated energy of PU received signal with a predefined value. If the calculated signal energy is less than threshold value it is pretended as the absence of PU and in the other case spectrum is assumed as occupied by PU. Decoding PU signal at relays before forwarding towards destination provide better performance in terms of detection probability and decreasing probability of false alarming as the Signal to Noise (SNR) increases. The proposed cooperative spectrum sensing using DF protocol at cognitive relays with Alamouti STBC is implemented and results are validated by MATLAB simulation. / +46 455 38 50 00

This paper uses Maximum likelihood estimation method to estimate the common correlation coefficients for multivariate datasets. We discuss a graphical tool, Q-Q plot, to test equality of the common intraclass correlation coefficients. Kolmogorov-Smirnov test and Cramér-von Mises test are used to check if the intraclass correlation coefficients are the same among populations. Bootstrap and empirical likelihood methods are applied to construct the confidence interval of the common intraclass correlation coefficients.

Model-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes. The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations. Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed. Through a practical two-phase nylon reactor example, as well as several simulation studies using stirred tank reactors, it is shown that the proposed parameter estimation algorithm can address difficulties such as: different types of measured responses with different levels of measurement noise, measurements taken at irregularly-spaced sampling times, unknown initial conditions for some state variables, unmeasured state variables, and unknown disturbances that enter the process and influence its future behaviour. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-06-20 16:34:44.586

This dissertation consists of three essays that study mergers and consolidation in the Canadian cable television industry. The first essay provides a historical overview of regulatory and technical change in the industry, and presents the dataset that I constructed for this study. The basic pattern of interest in the data is regional consolidation, where dominant cable companies grow over time by acquiring the cablesystems of small cable operators. I perform a reduced-form empirical analysis that formally studies the determinants of mergers, and the effect that acquisitions have on cable bundles offered to consumers. The remaining essays develop and estimate structural econometric models to further study the determinants and welfare consequences of mergers in the industry. The second essay estimates an empirical analogue of the Farrell and Scotchmer (1988) coalition- formation game. I use the estimated model to measure the equilibrium impact that economies of scale and agglomeration has on firms’ acquisition incentives. I also study the impact entry and merger subsidies have on consolidation and long-run market structure. The final chapter estimates a variant of the Rochet and Stole (2002) model of multi-product monopoly with endogenous quality and prices. Using the estimated model I compute the impact mergers have on welfare. I find that both consumer and producer surplus rise with acquisitions. I also show that accounting for changes both in prices and products (i.e., cable bundle quality) is important for measuring the welfare impact of mergers. / Thesis (Ph.D, Economics) -- Queen's University, 2010-12-09 14:39:15.431

The analysis of survival data with a possible cure fraction has attracted much interest in the last two decades. Various models and estimating methods have been proposed for such data and they have been applied in many fields, especially in cancer clinical trials. In the thesis, we consider some new general cure models, which include existing survival models as their special cases. We also consider a nonparametric estimation of cure rate. The estimator is proved consistent and asymptotically normal. We also consider the application of proportional density for cure data and the analysis of length-biased cure data. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2012-01-26 09:53:08.127

In this work wireless communication systems with multiple co-channel signals present at the receiver are considered. One of the major challenges in the development of such systems is the computational complexity required for the detection of the transmitted signals. This thesis addresses this problem and develops reduced complexity algorithms for the detection of multiple co-channel signals in receivers with multiple antennas. The signals are transmitted from either a single user employing multiple transmit antennas, from multiple users or in the most general case by a mixture of the two. The receiver is assumed to be overloaded in that the number of transmitted signals exceeds the number of receive antennas. Joint Maximum Likelihood (JML) is the optimum detection algorithm which has exponential complexity in the number of signals. As a result, detection of the signals of interest at the receiver is challenging and infeasible in most practical systems. The thesis presents a framework for the detection of multiple co-channel signals in overloaded receivers. It proposes receiver structures and two list-based signal detection algorithms that allow for complexity reduction compared to the optimum detector while being able to maintain near optimum performance. Complexity savings are achieved by first employing a linear preprocessor at the receiver to reduce the effect of Co-Channel Interference (CCI) and second, by using a detection algorithm that searches only over a subspace of the transmitted symbols. Both algorithms use iterative processing to extract ordered lists of the most likely transmit symbols. Soft information can be obtained from the detector output list and can then be used by error control decoders. The first algorithm named Parallel Detection with Interference Estimation (PD-IE) considers the Additive White Gaussian Noise (AWGN) channel. It relies on a spatially reduced search over subsets of the transmitted symbols in combination with CCI estimation. Computational complexity under overload is lower than that of JML. Performance results show that PD-IE achieves near optimum performance in receivers with Uniform Circular Array (UCA) and Uniform Linear Array (ULA) antenna geometries. The second algorithm is referred to as List Group Search (LGS) detection. It is applied to overloaded receivers that operate in frequency-flat multipath fading channels. The List Group Search (LGS) detection algorithm forms multiple groups of the transmitted symbols over which an exhaustive search is performed. Simulation results show that LGS detection provides good complexity-performance tradeoffs under overload. A union bound for group-wise and list-based group-wise symbol detectors is also derived. It provides an approximation to the error performance of such detectors without the need for simulation. Moreover, the bound can be used to determine some detection parameters and tradeoffs. Results show that the bound is tight in the high Signal to Interference and Noise Ratio (SINR) region.

This thesis concerns inference problems in balanced random effects models with a so-called block circular Toeplitz covariance structure. This class of covariance structures describes the dependency of some specific multivariate two-level data when both compound symmetry and circular symmetry appear simultaneously. We derive two covariance structures under two different invariance restrictions. The obtained covariance structures reflect both circularity and exchangeability present in the data. In particular, estimation in the balanced random effects with block circular covariance matrices is considered. The spectral properties of such patterned covariance matrices are provided. Maximum likelihood estimation is performed through the spectral decomposition of the patterned covariance matrices. Existence of the explicit maximum likelihood estimators is discussed and sufficient conditions for obtaining explicit and unique estimators for the variance-covariance components are derived. Different restricted models are discussed and the corresponding maximum likelihood estimators are presented. This thesis also deals with hypothesis testing of block covariance structures, especially block circular Toeplitz covariance matrices. We consider both so-called external tests and internal tests. In the external tests, various hypotheses about testing block covariance structures, as well as mean structures, are considered, and the internal tests are concerned with testing specific covariance parameters given the block circular Toeplitz structure. Likelihood ratio tests are constructed, and the null distributions of the corresponding test statistics are derived.

This dissertation develops influence diagnostics for crossover models. Mixed linear models and generalised mixed linear models are utilised to investigate continuous and count data from crossover studies, respectively. For both types of models, changes in the maximum likelihood estimates of parameters, particularly in the estimated treatment effect, due to minor perturbations of the observed data, are assessed. The novelty of this dissertation lies in the analytical derivation of influence diagnostics using decompositions of the perturbed mixed models. Consequently, the suggested influence diagnostics, referred to as the delta-beta and variance-ratio influences, provide new findings about how the constructed residuals affect the estimation in terms of different parameters of interest. The delta-beta and variance-ratio influence in three different crossover models are studied in Chapters 5-6, respectively. Chapter 5 analyses the influence of subjects in a two-period continuous crossover model. Possible problems with observation-level perturbations in crossover models are discussed. Chapter 6 extends the approach to higher-order crossover models. Furthermore, not only the individual delta-beta and variance-ratio influences of a subject are derived, but also the joint influences of two subjects from different sequences. Chapters 5-6 show that the delta-beta and variance-ratio influences of a particular parameter are decided by the special linear combination of the constructed residuals. In Chapter 7, explicit delta-beta influence on the estimated treatment effect in the two-period count crossover model is derived. The influence is related to the Pearson residuals of the subject. Graphical tools are developed to visualise information of influence concerning crossover models for both continuous and count data. Illustrative examples are provided in each chapter.