Modelling and Analysis of Recurrent Event Data with Multiple Causes

This thesis Entitled “modelling and analysis of recurrent event data with multiple causes.Survival data is a term used for describing data that measures the time to occurrence of an event.In survival studies, the time to occurrence of an event is generally referred to as lifetime.Recurrent event data are commonly encountered in longitudinal studies when individuals are followed to observe the repeated occurrences of certain events. In many practical situations, individuals under study are exposed to the failure due to more than one causes and the eventual failure can be attributed to exactly one of these causes.The proposed model was useful in real life situations to study the effect of covariates on recurrences of certain events due to different causes.In Chapter 3, an additive hazards model for gap time distributions of recurrent event data with multiple causes was introduced. The parameter estimation and asymptotic properties were discussed .In Chapter 4, a shared frailty model for the analysis of bivariate competing risks data was presented and the estimation procedures for shared gamma frailty model, without covariates and with covariates, using EM algorithm were discussed. In Chapter 6, two nonparametric estimators for bivariate survivor function of paired recurrent event data were developed. The asymptotic properties of the estimators were studied. The proposed estimators were applied to a real life data set. Simulation studies were carried out to find the efficiency of the proposed estimators.

Surface Circulation in the Tropical Indian Ocean Using Multi-Mission Altimeter Data

In the present study the availability of satellite altimeter sea level data with good spatial and temporal resolution is explored to describe and understand circulation of the tropical Indian Ocean. The derived geostrophic circulations showed large variability in all scales. The seasonal cycle described using monthly climatology generated using 12 years SSH data from 1993 to 2004 revealed several new aspects of tropical Indian Ocean circulation. The interannual variability presented in this study using monthly means of SSH data for 12 years have shown large year-to-year variability. The EOF analysis has shown the influence of several periodic signals in the annual and interannual scales where the relative strengths of the signals also varied from year to year. Since one of the reasons for this kind of variability in circulation is the presence of planetary waves. This study discussed the influence of such waves on circulation by presenting two cases one in the Arabian Sea and other in the Bay of Bengal.

Studies on the macrobenthic community of Cochin backwaters with special reference to culture of Eriopisa chilkensis (Gammaridae-Amphipoda)

This thesis Entitled studies on the macrobenthic community of cochin backwaters with special reference to culture of eriopisa chilkensis (Gammaridae- amphipoda).Benthic organisms are usually studied for environmental impact assessment, pollution control and resource conservation. The benthic monitoring component has three major objectives: 1) characterize the benthic communities to assess the estuarine health, 2) determine seasonal and spatial variability in benthic communities, and 3) detect changes in the estuarine community through examination of changes in abundances of specific indicator taxa and other standard benthic indices.Cochin backwaters situated at the tip of the northern Vembanad lake is a tropical positive estuarine system. The backwaters of Kerala support as much biological productivity and diversity as tropical rain forest and are responsible for the rich fishery potential of Kerala. Backwaters also act as nursery grounds for commercially important prawns and fishes.The thesis has been subdivided into seven chapters. The first chapter gives a general introduction about the topic and also highlights the scope and purpose of the study. The second chapter covers the methodology adopted for the collection and analysis of water quality parameters, sediment and the macrobenthic fauna.Chapter 3 deals with hydrographic features, sediment characteristics and the spatial variation and abundance of macrobenthic fauna in the Cochin estuary.Chapter 4 explains the impact of organic enrichment on macrobenthic popUlation in the Cochin estuary and includes the comparison of the present data with the earlier work in this region.Chapter 5 deals with seasonal variability in abundance of macrobenthic species in the estuary. The study was conducted from 9 stations during three seasons (pre-monsoon, monsoon and post-monsoon) in 2003.Chapter 6 deals with Life history and Population Dynamics of Eriopisa chilkensis Chilton (Gammaridae-Amphipoda). The life cycle of the gammarid amphipod Eriopisa chilkensis from the Cochin estuary, south west coast of India was studied for the first time under laboratory conditions.

SPATIAL CLUSTERING ALGORITHMS-AN OVERVIEW.

An Overview of known spatial clustering algorithms The space of interest can be the two-dimensional abstraction of the surface of the earth or a man-made space like the layout of a VLSI design, a volume containing a model of the human brain, or another 3d-space representing the arrangement of chains of protein molecules. The data consists of geometric information and can be either discrete or continuous. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, spatial data mining algorithms are required for spatial characterization and spatial trend analysis. Spatial data mining or knowledge discovery in spatial databases differs from regular data mining in analogous with the differences between non-spatial data and spatial data. The attributes of a spatial object stored in a database may be affected by the attributes of the spatial neighbors of that object. In addition, spatial location, and implicit information about the location of an object, may be exactly the information that can be extracted through spatial data mining