Local Trigonometric Methods for Time Series Smoothing.

The thesis is concerned with local trigonometric regression methods. The aim was to develop a method for extraction of cyclical components in time series. The main results of the thesis are the following. First, a generalization of the filter proposed by Christiano and Fitzgerald is furnished for the smoothing of ARIMA(p,d,q) process. Second, a local trigonometric filter is built, with its statistical properties. Third, they are discussed the convergence properties of trigonometric estimators, and the problem of choosing the order of the model. A large scale simulation experiment has been designed in order to assess the performance of the proposed models and methods. The results show that local trigonometric regression may be a useful tool for periodic time series analysis.

The analysis of the collapse of a precast r.c. industrial building during the 29th may 2012 emilia romagna earthquake

Analysis of the collapse of a precast r.c. industrial building during the 2012 Emilia earthquake, focus on the failure mechanisms in particular on the flexure-shear interactions. Analysis performed by a time history analysis using a FEM model with the software SAP2000. Finally a reconstruction of the collapse on the basis of the numerical data coming from the strength capacity of the elements failed, using formulation for lightly reinforced columns with high shear and bending moment.

Set up of a procedure for correlation analysis between current discharge and dose delivery in a pulsed power plasma device for medical application

This thesis work aims to find a procedure for isolating specific features of the current signal from a plasma focus for medical applications. The structure of the current signal inside a plasma focus is exclusive of this class of machines and a specific analysis procedure has to be developed. The hope is to find one or more features that shows a correlation with the dose erogated. The study of the correlation between the current discharge signal and the dose delivered by a plasma focus could be of some importance not only for the practical application of dose prediction but also for expanding the knowledge anbout the plasma focus physics. Vatious classes of time-frequency analysis tecniques are implemented in order to solve the problem.

Haematopoietic stem cell transplantation in Switzerland. Report from the Swiss Transplant Working Group Blood and Marrow Transplantation (STABMT) Registry 1997-2003

In 1997, the Swiss Transplant Working Group Blood and Marrow Transplantation (STABMT) initiated a mandatory national registry for all haematopoietic stem cell transplants (HSCT) in Switzerland. As of 2003, information was collected of 2010 patients with a first HSCT (577 allogeneic (29%) and 1433 autologous (71%) HSCT) and 616 additional re-transplants. This included 1167 male and 843 female patients with a median age of 42.4 years (range 0.2-76.6 years). Main indications were leukaemias (592; 29%) lymphoproliferative disorders (1,061; 53%), solid tumours (295; 15%) and non-malignant disorders (62; 3%). At the time of analysis 1,263 patients were alive (63%), 747 had died (37%). Probability of survival, transplant related mortality or relapse at 5 years was 52%, 21%, 36% for allogeneic and 54%, 5%, 60% for autologous HSCT. Outcome depended on indication, donor type, stem cell source and age of patient. HSCT is an established therapy in Switzerland. These data describe current practice and outcome.

Semiparametric Methods for Semi-competing Risks Problem with Censoring and Truncation

Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator for the gamma frailty parameter under left truncation. Using this estimator, we then derive a closed form estimator for the marginal distribution of the non-terminal event. The large sample properties of the estimators are established via asymptotic theory. The methodology performs well with moderate sample sizes, both in simulations and in an analysis of data from a diabetes registry.

Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicate for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.