Regression models for bivariate survival data

Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations.

Defect analysis of semiconductor thin films for photovoltaic applications using photo-luminescence and photo-conductivity

The present thesis can be divided into three areas:1) the fabrication of a low temperature photo-luminescence and photoconductivity measuring unit 2) photo-luminescence in the chalcopyrite CulnSez and CulnS2 system for defect and composition analysis and 3) photo-luminescence and photo-conductivity of In:JS3. This thesis shows that photo-luminescence is one of most essential semiconductor characterization tool for a scientific group working on photovoltaics. Tools which can be robust, non-destructive, requiring minimal sample preparation for analysis and most informative of the device applications are sought after by industries and this thesis is towards establishing photo-luminescence as "THE" tool for semiconductor characterization. The possible application of photo-luminescence as a tool for compositional and quality analysis of semiconductor thin films has been worked upon by this thesis. Photo-conductivity complement photo-luminescence and together they provide all the information required for the fabrication of an opto-electronic device.

Modelling and Analysis of Bivariate Lifetime Data using Reversed Hazard Rates

Department of Statistics, Cochin University of Science and Technology

Nonparametric Estimation of Survivor Function in Bivariate Competing Risk Models’

So far, in the bivariate set up, the analysis of lifetime (failure time) data with multiple causes of failure is done by treating each cause of failure separately. with failures from other causes considered as independent censoring. This approach is unrealistic in many situations. For example, in the analysis of mortality data on married couples one would be interested to compare the hazards for the same cause of death as well as to check whether death due to one cause is more important for the partners’ risk of death from other causes. In reliability analysis. one often has systems with more than one component and many systems. subsystems and components have more than one cause of failure. Design of high-reliability systems generally requires that the individual system components have extremely high reliability even after long periods of time. Knowledge of the failure behaviour of a component can lead to savings in its cost of production and maintenance and. in some cases, to the preservation of human life. For the purpose of improving reliability. it is necessary to identify the cause of failure down to the component level. By treating each cause of failure separately with failures from other causes considered as independent censoring, the analysis of lifetime data would be incomplete. Motivated by this. we introduce a new approach for the analysis of bivariate competing risk data using the bivariate vector hazard rate of Johnson and Kotz (1975).

Cinematographic Licensing

In India, film censorship originated with Cinematograph Act 1918 empowering the Provincial Governments to establish censorial authorities. In 1949, an amendment provided for a Central Board of Film Censors. In 1952, a new legislation gave the Central Government enormous powers, making the Board to function as a department of the Central Government. The Government had control over the Board with the mechanism of issuing 'directions' to the censors and laying down censorship rules. The legislation did not provide any objective criteria for censoring films. The 1959 amendment, aimed at curing this defect, only incorporated the grounds contained in Article 19(2) of the Constitution. Even after expert studies and a significant decision by the Supreme Court, pointing out the inadequacy of the existing system, and governmental attempts to bring reforms by way of fresh directions, appointment of appellate authority and framing of new rules, the system still warrants radical change. The thesis explores them.

‘Modeling and Analysis of Competing Risks Data’

there has been much research on analyzing various forms of competing risks data. Nevertheless, there are several occasions in survival studies, where the existing models and methodologies are inadequate for the analysis competing risks data. ldentifiabilty problem and various types of and censoring induce more complications in the analysis of competing risks data than in classical survival analysis. Parametric models are not adequate for the analysis of competing risks data since the assumptions about the underlying lifetime distributions may not hold well. Motivated by this, in the present study. we develop some new inference procedures, which are completely distribution free for the analysis of competing risks data.