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).

Analysis of Some Stochastic Inventory Models with Pooling/ Retrial of Customers

In this thesis we have presented several inventory models of utility. Of these inventory with retrial of unsatisfied demands and inventory with postponed work are quite recently introduced concepts, the latt~~ being introduced for the first time. Inventory with service time is relatively new with a handful of research work reported. The di lficuity encoLlntered in inventory with service, unlike the queueing process, is that even the simplest case needs a 2-dimensional process for its description. Only in certain specific cases we can introduce generating function • to solve for the system state distribution. However numerical procedures can be developed for solving these problem.

Depth wise variation of microbial load in the soils of midland region of Kerala: a function of important soil physicochemical characteristics and nutrients

Soil microorganisms play a main part in organic matter decomposition and are consequently necessary to soil ecosystem processes maintaining primary productivity of plants. In light of current concerns about the impact of cultivation and climate change on biodiversity and ecosystem performance, it is vital to expand a complete understanding of the microbial community ecology in our soils. In the present study we measured the depth wise profile of microbial load in relation with important soil physicochemical characteristics (soil temperature, soil pH, moisture content, organic carbon and available NPK) of the soil samples collected from Mahatma Gandhi University Campus, Kottayam (midland region of Kerala). Soil cores (30 cm deep) were taken and the cores were separated into three 10-cm depths to examine depth wise distribution. In the present study, bacterial load ranged from 141×105 to 271×105 CFU/g (10cm depth), from 80×105 to 131×105 CFU/g (20cm depth) and from 260×104 to 47×105 CFU/g (30cm depth). Fungal load varies from 124×103 to 27×104 CFU/g, from 61×103 to110×103 CFU/g and from 16×103 to 49×103 CFU/g at 10, 20 and 30 cm respectively. Actinomycetes count ranged from 129×103 to 60×104 CFU/g (10cm), from 70×103 to 31×104 CFU/g (20cm) and from 14×103 to 66×103 CFU/g (30cm). The study revealed that there was a significant difference in the depthwise distribution of microbial load and soil physico-chemical properties. Bacterial, fungal and actinomycetes load showed a decreasing trend with increasing depth at all the sites. Except pH all other physicochemical properties showed decreasing trend with increasing depth. The vertical profile of total microbial load was well matched with the depthwise profiles of soil nutrients and organic carbon that is microbial load was highest at the soil surface where organics and nutrients were highest

Quantile based entropy function

Quantile functions are efficient and equivalent alternatives to distribution functions in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). Motivated by this, in the present paper, we introduce a quantile based Shannon entropy function. We also introduce residual entropy function in the quantile setup and study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the residual quantile entropy function determines the quantile density function uniquely through a simple relationship. The measure is used to define two nonparametric classes of distributions

Macrobenthos And Its Relation To Ecosystem Dynamics In The Cochin Estuary

Cochin estuary is a shallow brackish water body situated on the south west coast of India. It is a tropical positive estuary extending between 90 40’ and 100 12’ N and 760 10’and 760 30’ E with its northern boundary at Azhikode and southern boundary at Thannermukkom bund.The abundance of benthic fauna in an ecosystem shows the close relationship to its environment and reflects the characteristics of an ecological niche. Seasonal and monthly variations in the distribution of macrobenthos in relation to sediment characteristics were conducted in Cochin estuary from 2009-10 periods. Oxidation-reduction potential showed reducing trends that affected the distribution and diversity of fauna. Seasonal variations in water quality and river discharge pattern affected the faunal composition in the different stations. Sewage mixing was the principal source of organic pollution in the Cochin estuary. The sediment pH was generally on the alkaline side ranging from 4.99 at St.9 and 8.33 at St.1.The Eh ranged from -11mV at St.3 to -625mV at St.2.The temperature varied from 260C to 320C in the estuary. The moisture content ranged from 1.63 to 12.155%, that of organic carbon from 0 09 at St. 6 to 4.29% at St.9 and that of organic matter from 0.16 to 7.39%. Seasonally, the average of Eh was highest during the monsoon (156.22 mV) and in the pre monsoon (140.94 mV). The average pH for the 9 study stations was 7.68 during monsoon period and 7.08 during post monsoon. Based on group wise seasonal analysis, the average mean abundance was maximum for polychaetes (43.47) followed by nematodes (33.62), crustaceans (21.62), molluscs (11.94) and Pisces (0.05) in the estuary. Monsoon season was most favourable for benthic faunal abundance followed by the post monsoon period in the study. The series of human interventions like dredging, discharge of industrial effluents, urbanisation and related aspects had a strong influence on the distribution, abundance of benthic macrofauna in the wetland.