Concomitants of Order Statistics from Morgenstern Family

The study deals with the distribution theory and applications of concomitants from the Morgenstern family of bivariate distributions.The Morgenstern system of distributions include all cumulative distributions of the form FX,Y(X,Y)=FX(X) FY(Y)[1+α(1-FX(X))(1-FY(Y))], -1≤α≤1.The system provides a very general expression of a bivariate distributions from which members can be derived by substituting expressions of any desired set of marginal distributions.It is a brief description of the basic distribution theory and a quick review of the existing literature.The Morgenstern family considered in the present study provides a very general expression of a bivariate distribution from which several members can be derived by substituting expressions of any desired set of marginal distributions.Order statistics play a very important role in statistical theory and practice and accordingly a remarkably large body of literature has been devoted to its study.It helps to develop special methods of statistical inference,which are valid with respect to a broad class of distributions.The present study deals with the general distribution theory of Mk, [r: m] and Mk, [r: m] from the Morgenstern family of distributions and discuss some applications in inference, estimation of the parameter of the marginal variable Y in the Morgestern type uniform distributions.

Influence of short term climatic fluctuations on the agricultural land use in Kerala

In the present study, the land use over Kerala State and its spatial and temporal variations, spatio-temporal variations of water budget elements, climatic shifts, incidence of droughts and the influence of inter-annual fluctuations of rainfall on area. production and yield of selected crops, have been studied in detail. The thesis consists of seven chapters including the introduction. The first section of the Second Chapter deals with the importance of agrocliinatological studies in general and its application in agricultural land use in particular. It also gives an overview of the short term climatic fluctuations, water balance studies, crop weather relationships, land use patterns and various agricultural indices. This includes a detailed review of available literature in this field. The basic concepts. data used and the methodology adopted in the study forms, the second section of this Chapter. The Third Chapter gives the details of the physical features of the State such as the relief, geology, geomorphologysoils, drainage, and vegetation. The agroclimatology of the State is discussed in detail in Chapter Four. The first Section presents annual and seasonal variations of temperature and rainfall of the State along with a discussion on the water balance of the State. The secondSection of this Chapter deals with the influence of rainfall and water balance elements on various crops. The district-wise general land use pattern of theState and its spatio-temporal variations are discussed in Chapter Five. The first Section of Chapter Six gives an overview of the agricultural land use pattern of the State, cropping patterns, cropping intensity, crop combination and their spatio-temporal variations. The inter-annual variability of water balances of various stations of the State computed using the method of Thornthwaite (1948) and Thornthwaite & Mather (1955) is presented in the second Section of Chapter Six. This also includes a discussion of how the climatic shifts have occurred over the State and the influence of variations of climatic and water balance elements on the crops. The Seventh Chapter gives the summary of the work carried out and the results obtained from the study. Interpretations of the results, conclusions and suggestions made,based on the observations of the study are incorporated in this Chapter.

Impact of Agricultural Credit in the.Agricultural Development of Kuttanad

Introduction of agrarian reforms and introduction of new technology increased dependence on casual labourers. High labour absorption in the subsistence agriculture and increased price of input resulted in high cost of cultivation. Price of paddy did not rise correspondingly. As a result subsistence economy's future is bleak. The purpose of the _study is to examine these arguments and related issues with the help of empirical evidence from Kuttanad. The credit schemes are designed to help farmers to earn higher incomes by larger output brought either by an increase in area or by an improvement in yield rates or both. It is difficult to isolate the impact of agricultural credit on agricultural development. Because agricultural development is the combined effect of all inputs. The specific .criteria selected for analysing the impact of agricultural icredit are how increased supply of credit would bring changes ‘in capital formation, agrarian relations, informal lending and its cost and the changes in area, output, introduction of new technology, income, savings and employment of farm households.

Pretreatment Of Agricultural Waste With Pleurotus Sp. For Ethanol Production

Bioethanol is a liquid fuel obtained from fermentation of sugar/starch crops. Lignocellulosic biomass being less expensive is considered a future alternative for the food crops. One of the main challenges for the use of lignocellulosics is the development of an efficient pre-treatment process. Pretreatments are classified into three - physical, chemical, and biological pretreatment. Chemical process has not been proven suitable so far, due to high costs and production of undesired by-products. Biologically, hydrolysis can be enhanced by microbial or enzymatic pretreatment. Studies show that the edible mushrooms of Pleurotus sp. produce several extracellular enzymes which reduce the structural and chemical complexity of fibre. In the present study, P. ostreatus and P. eous were cultivated on paddy straw. Spent substrate left after mushroom cultivation was powdered and used for ethanol production. Saccharomyces sp. was used for fermentation studies. Untreated paddy straw was used as control. Production of ethanol from P. ostreatus substrate was 5.5 times more when compared to untreated paddy straw, while the spent substrate of P. eous gave 5 times increase in ethanol yield. Assays showed the presence of several extracellular enzymes in the spent substrate of both species, which together contributed to the increase in ethanol yield

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.