In this study, three different MR pulse sequences, IR-FSE, MP2RAGE, and firstly proposed MP3RAGE, were applied to obtain high-resolution 3D T1 mapping of whole brain at 1.5 Tesla. Among these three sequences, MP2RAGE uses fast gradient echo as readout module. Signals of two different inversion times are acquired at once and can be used to calculate T1 relaxation time according to Bloch equation. However, the magnetization was also influenced by the excitation efficiency of inversion adiabatic pulse, which was usually estimated by numerical simulation and taken as a constant over the field of view in the literature. However, this might not be true in practice. Therefore, a newly modified pulse sequence, MP3RAGE, was proposed to acquire data of three distinct inversion times without increasing scanning time. As a result, the spatial distribution of T1 and inversion efficiency can be assessed by solving nonlinear least square problem. In addition, the IR-FSE sequence with six inversion times was also applied in every experiment to provide T1 value for reference. Results showed that the T1 estimation obtained by MP2RAGE is close to, but slightly lower than that by IR-FSE, which is in agreement with those reported in literatures. In addition, the 3D high-resolution maps of T1 and efficiency were successfully estimated with the use of MP3RAGE. Spatial smoothing on inversion efficiency helps reducing the sensitivity to noise in the nonlinear approach, leading to T1 values closer to those by IR-FSE.

Images may be degraded by some random error which is called noise. Noise may occur during image capture, transmission or processing and its elimination is achieved by smoothing filters. Linear smoothing filters blur the edges and edges are important characteristics of images and must be preserved. Various edge preserving smoothing filters are proposed in the literature. In this thesis, most common smoothing and edge preserving smoothing filters are discussed and a new method is proposed by modifying Ambrosio Tortorelli approximation of Mumford Shah Model. New method takes into edge direction consistency account and produces sharper results at comparable scales.

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In biomedical research and lifetime data analysis, the comparison of two hazard functions usually plays an important role in practice. In this thesis, we consider the standard independent two-sample framework under right censoring. We construct efficient and useful confidence intervals for the ratio and difference of two hazard functions using smoothed empirical likelihood methods. The empirical log-likelihood ratio is derived and its asymptotic distribution is a chi-squared distribution. Furthermore, the proposed method can be applied to medical diagnosis research. Simulation studies show that the proposed EL confidence intervals have better performance in terms of coverage accuracy and average length than the traditional normal approximation method. Finally, our methods are illustrated with real clinical trial data. It is concluded that the empirical likelihood methods provide better inferential outcomes.

This dissertation examines the effect of income smoothing on information uncertainty, stock returns, and cost of equity. Following existing literature, I construct two income smoothing measures - capturing income smoothing through both total accruals and discretionary accruals. I show that income smoothing tends to reduce firms' information uncertainty, as measured by stock return volatility, analyst forecast dispersion, and analyst forecast error. Further, I provide evidence that market prices income smoothing and rewards income smoothing firms with a premium. Controlling for unexpected earnings shocks and other firm characteristics, income smoothing firms have significantly higher abnormal returns around earnings announcement. Finally, I show that income smoothing, particularly through discretionary accruals, reduces firms' implied cost of equity.

In biomedical research and lifetime data analysis, the comparison of two hazard functions usually plays an important role in practice. In this thesis, we consider the standard independent two-sample framework under right censoring. We construct efficient and useful confidence intervals for the ratio and difference of two hazard functions using smoothed empirical likelihood methods. The empirical log-likelihood ratio is derived and its asymptotic distribution is a chi-squared distribution. Furthermore, the proposed method can be applied to medical diagnosis research. Simulation studies show that the proposed EL confidence intervals have better performance in terms of coverage accuracy and average length than the traditional normal approximation method. Finally, our methods are illustrated with real clinical trial data. It is concluded that the empirical likelihood methods provide better inferential outcomes.

Capture-recapture (CRC) models use two or more samples, or lists, to estimate the size of a population. In the canonical example, a researcher captures, marks, and releases several samples of fish in a lake. When the fish that are captured more than once are few compared to the total number that are captured, one suspects that the lake contains many more uncaptured fish. This basic intuition motivates CRC models in fields as diverse as epidemiology, entomology, and computer science. We use simulations to study the performance of conventional log-linear models for CRC. Specifically we evaluate model selection criteria, model averaging, an asymptotic variance formula, and several small-sample data adjustments. Next, we argue that interpretable models are essential for credible inference, since sets of models that fit the data equally well can imply vastly different estimates of the population size. A secondary analysis of data on survivors of the World Trade Center attacks illustrates this issue. Our main chapter develops local log-linear models. Heterogeneous populations tend to bias conventional log-linear models. Post-stratification can reduce the effects of heterogeneity by using covariates, such as the age or size of each observed unit, to partition the data into relatively homogeneous post-strata. One can fit a model to each post-stratum and aggregate the resulting estimates across post-strata. We extend post-stratification to its logical extreme by selecting a local log-linear model for each observed point in the covariate space, while smoothing to achieve stability. Local log-linear models serve a dual purpose. Besides estimating the population size, they estimate the rate of missingness as a function of covariates. Simulations demonstrate the superiority of local log-linear models for estimating local rates of missingness for special cases in which the generating model varies over the covariate space. We apply the method to estimate bird species richness in continental North America and to estimate the prevalence of multiple sclerosis in a region of France.

År 2005 implementerades IFRS 3 och IAS 36 inom EU. Införandet innebär att företag årligen ska nedskrivningspröva goodwillvärdet istället för att linjärt skriva av posten under dess beräknade nyttjandeperiod. Nedskrivningsprövningen baseras på företagsledningens bedömningar om framtiden och flertalet studier har belyst att det nya regelverket lett till att goodwillposten utnyttjas opportunistiskt för att manipulera det rapporterade resultatet. I denna uppsats undersöks om det i en svensk kontext finns indikationer på att goodwillposten utnyttjas för artificial smoothing. Vi gör det genom att undersöka sambandet mellan goodwillintensitet och resultatvolatilitet och sambandet mellan onormalt stora vinster och goodwillnedskrivningar. Undersökningen visar att det föreligger ett signifikant negativt samband mellan goodwillintensitet och resultatvolatilitet och att det därmed finns indikationer på att goodwillposten utnyttjas för artificial smoothing. Studien visar också att företag som gör onormalt stora resultat inte gör relativt större nedskrivningar av goodwill än företag som gör normala resultat eller förluster. Resultatet indikerar att artificial smoothing, i den mån det praktiseras på Stockholmsbörsen, primärt utnyttjas i syfte att minska förluster och inte för att utjämna onormalt bra resultat.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (PhD)-Open University. BLDSC no.DX205389.