Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

INVESTIGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY

The thesis entitled INVESTIDGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY embodies the results of the investigations carried out on the solvent extraction separation of iron (III) vanadium(V) and titanium (IV) chlorides from the waste chloride liquors of titanium minerals processing industry by employing tributylphosphate (TBT) as an extractant. The objective of this study is to generate the knowledge base to achieve the recovery of iron, vanadium and titanium cvalues from multi- metal waste chloride liquors originating from ilmenite mineral beneficiation industries through selective separation and value added material development

HEAVY METAL TOXICITY IN BIVALVE- HISTOLOGICAL AND HISTOCHEMICAL ENQUIRY

Division of Marine Biology, Microbiology and Biochemistry, School of Marine Sciences, Cochin University of Science and Technology

BIOCHEMICAL GENETIC STUDIES ON THE OIL SARDINE, Sardinella longiceps (CUVIER AND VALENCIENNES, 1847) FROM SELECTED CENTRES OF THE WEST COAST OF INDIA

The main objective of the present investigation was to study the biochemical genetic variability within the species and genetic structure of its regional populations from west coast. Realising the recent report of occurrence of oil sardine fishery in east coast of India, population samples from Mandapam and Madras were also included in the present investigation. The original data gathered on the population genetics of the species have helped to interpret and evaluate the results objectively. The important conclusions drawn from a detailed discussions on the subject would throw some light on the probable process of problematic fluctuations in the abundance of oil sardine fishery of India. The academic and applied values of present discoveries need not be emphasised. The data used for the doctoral thesis were generated during the ICAR ad-hoc project on the "Population genetic studies on oil sardine, sardinella longiceps to identity distinct genetic stocks", carried out at CMFRI, Cochin during the years, 1988-1991

Nitrifying Bioreactors Integrated into Shrimp and Prawn Hatchery Systems: Molecular Characterization of the Nitrifying Consortia,Reactor Kinetics, Modeling and Validation

National Centre for Aquatic Animal Health, Cochin University of Science and Technology

Studies on the effect of toxic heavy metal mercury on the physiology and biochemistry of an estuarine crab Scylla serrata (Forskal)

This thesis Entitled studies on the effect of toxic heavy metal mercury on the physiology and biochemistry of an estuarine crab scylla serrata (Forskal). Evaluate the toxicity of three sub lethal concentrations of mercury, viz., 0.009 mg/l, 0.02 mg/1, and 0.04 mg/l on the mud crab, Scylla serrata through bioaccumulation, and depuration studies. To characterize the biochemical responses to the sub-lethal stress of mercury in chelate muscles, abdominal muscles, hepatopancreas and gills. To study the activity pattern of acid and alkaline phosphatases in mercury-exposed crabs. To evaluate the induced changes in these tissues through histopathological studies,The Cochin backwaters is one of the most productive and biologically active backwater systems, and is the habitat of varieties of fishes, mollusks, and crustaceans, though this water body also receives tons of effluents from factories located on the banks of the river, Periyar.To study the activity levels of acid and alkaline phosphatases in crabs, at three time periods, exposed to three sub lethal concentration of mercury,

‘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.

Some Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal Operations

When simulation modeling is used for performance improvement studies of complex systems such as transport terminals, domain specific conceptual modeling constructs could be used by modelers to create structured models. A two stage procedure which includes identification of the problem characteristics/cluster - ‘knowledge acquisition’ and identification of standard models for the problem cluster – ‘model abstraction’ was found to be effective in creating structured models when applied to certain logistic terminal systems. In this paper we discuss some methods and examples related the knowledge acquisition and model abstraction stages for the development of three different types of model categories of terminal systems

Agent Based Evacuation Simulation Using Leader-Follower Model

Agent based simulation is a widely developing area in artificial intelligence.The simulation studies are extensively used in different areas of disaster management. This work deals with the study of an agent based evacuation simulation which is being done to handle the various evacuation behaviors.Various emergent behaviors of agents are addressed here. Dynamic grouping behaviors of agents are studied. Collision detection and obstacle avoidances are also incorporated in this approach.Evacuation is studied with single exits and multiple exits and efficiency is measured in terms of evacuation rate, collision rate etc.Net logo is the tool used which helps in the efficient modeling of scenarios in evacuation