From a general unequal probability sample a standard estimator for Karl Pearson?s product-moment correlation coefficient between two variables in a finite population is taken as a non-linear function of unbiased estimators respectively for six specific population totals. By Taylor series expansion an approximate variance estimator for it is also available. The corresponding Spearman?s rank correlation coefficient has no such facility because sample ranks bear no discernible relations to individual-wise population ranks. But Kendall?s rank correlation coefficient ?Tau? has no such shortcoming. Rather, it is still simpler involving only totals of three variables, instead of six? and the corresponding estimators. Applying Taylor series expansion its accuracy level is examined. Simulation-based numerical results are also presented that look encouraging. Keywords: Linearization, Product-moment correlation coefficient, Rank correlation, Unequal probability sampling.

  India claims to have achieved Millennium Development Goal (MDG) of reducing poverty by half from the level of 47.8% in 1990 to 21.9% in 2011?12. But the very definition of Poverty Line with the norm of Rs. 26 per capita per day in rural and Rs. 32 in urban is seen to be too low to be acceptable. The report by Rangarajan Committee which revised upward the Poverty Line to Rs. 32 for rural and Rs. 47 for urban has generated a fresh debate as the raise is viewed as marginal. In view of this, NITI Aayog constituted a Task Force on Elimination of Poverty in India with the mandate to develop a working definition of poverty line and to prepare a roadmap for the elimination of poverty. The issues which are required to be addressed by the Task Force in developing a working definition are mainly Calorie and/or other Criteria to be used for defining Poverty Line and the Choice of Base Year. An analysis of change in Consumption Pattern, Age & Occupation Structure of the population, Recommended Dietary Allowances etc revealed that the implication of these in constructing a working definition of poverty line involves a multi faceted approach. Importantly, the poverty line should ensure all basic / minimum needs. Keywords: Calorie norm, Minimum needs, Occupation structure, Per capita consumption expenditure, Population pyramid, Recommended dietary allowances.

  In this study an attempt has been made to map hunger in Gram Panchayats of Banda district. The Gram Panchayats have been identified as food secure, food insecure without hunger and food insecure with hunger on the basis of MFAST which is a modified version of FAST and anthropometric measurements and related indicators. The districts were mapped as per the criterion of food insecure with hunger. The study has revealed that in general, the study area is vulnerable to hunger situations. Infrastructure relating to all the sectors like, agriculture, health education etc. is poor across the Panchayat Groups. Dietary intake and nutritional status were also found to be in deplorable state. Similarly, socio economic indicators and household characteristics like type of houses, source of drinking water, economic status, land holdings, irrigation facilities, PDS and MNREGA present a very poor scenario. Keywords: FANTA, FAST, Food insecure, Hunger, MFAST.

  In many applications, the relationship between the dependent variable and an independent regressor is non-linear in parameters. In such situations, we do not get optimum estimates of parameters in closed form and various non-linear optimization algorithms are used to obtain the optimum estimates. These algorithms are iterative in nature and need good initial estimates of parameters as seed values for a faster and global convergence. This paper proposes various methods based on finite differences to estimate the parameters of non-linear models belonging to the asymptotic regression category. Some published data sets are used to illustrate the application of the proposed methods. It has been demonstrated that the proposed methods produce efficient initial estimates for optimization algorithms. Keywords: Asymptotic regression, Non-linear regression, Finite differences, Growth curves, Optimization algorithms.

  The present study proposed a new hybrid model combining Autoregressive integrated moving average (ARIMA) and Wavelet Neural Network (WNN). ARIMA is the most widely used technique for forecasting in divergent domains for several decades. WNN is the recently developed neural networks which utilize wavelet activation function in the hidden neuron. As a case study, wheat yield of India has been considered to evaluate the forecasting performance of the proposed hybrid model. The proposed method was compared with ARIMA and existing hybrid ARIMA-ANN approach. Empirical results clearly reveal that the forecasting accuracy of the proposed method is better as compared to the existing approach. Keywords: Forecasting, ARIMA, ANN, WNN, Hybrid model.

  The Standardized Precipitation Index (SPI) is used throughout the world as a meteorological drought index to identify the duration and/or severity of drought. Early forecasting of drought is a critical issue to mitigate the adverse effects of drought of varying intensities. To address this issue, linear stochastic models, such as ARIMA and SRIMA have been used in this study. We studied ARIMA and SARIMA models to identify the most appropriate model to describe the SPI series at 3, 6, 9, 12 and 24 month time scale for the Ballary region in Southern India. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicated that the region has been affected by a prolonged drought during the study period (1968?2012). Our study followed ARIMA calibration approach using time series data of SPI series for drought forecasting. The best model among different data sets has been identified using minimum Akaike Information Criteria (AIC), Schwarz-Bayesian Information Criteria (SBC) criteria along with the independency and normality criteria of the residuals. For 3-month SPI series ARIMA was observed to be appropriate while SARIMA model series is promising for the remaining SPI series. The stochastic models developed to predict drought were observed to give reasonably good results with 3 month lead time. Since drought prediction plays an important role in conservation of water resources, water storage management and mitigating drought severity, stochastic models has been observed to be the best and is recommended for drought forecasting in this region of India. Keywords: Auto regressive integrated moving average, Drought forecasting, Linear stochastic model, Seasonal auto regressive integrated moving average, Southern India, Standardized precipitation index.

  integrated moving average, Southern India, Standardized precipitation index. as well as in agricultural field for providing easy accessibility, efficiency and quality of learning. E-learning has become a new paradigm to make available the knowledge in our society. Dog Health Management Trainer (DHMT) has been developed to disseminate the information on various aspects of dog health. The system has been evolved using latest web technologies including JSON, CSS3 and JavaScript. The system comprises of six different components viz., user interface, multimedia enabled interface, database, knowledge acquisition, domain expert and user administration. The user interface has been categorized into four modules i.e., breed database, general information, breeding & health care and vaccination and deworming. The system provides stepwise information to end users regarding various important aspects related to dog health. The end users explore the required information as they desired. The system was evaluated to find the usability in the IVRI Polyclinic visitors covering 100 dog owners. The results of the survey reveal that 87% dog owners have shown the keen interest to use and procure the system. Keywords: eLearning, Dog, Breeds, Feeds, Health, Web browser.

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  In this paper, forecasting performance of time-delay neural network and GARCH models for predicting the volatility using monthly price series of edible oils in domestic and international markets is evaluated. An attempt has also been made to investigate whether the forecasting performance of two competing models can be improved by combining their individual forecasts. For this purpose, the individual models were combined to produce improved forecasts using non-parametric approach through the use of purpose, the individual models were combined to produce improved forecasts using non-parametric approach through the use of Keywords: Time-delay neural network, GARCH, Non-parametric, Combining forecasts.

  Regression type estimators of the population total were developed using the calibration approach under the assumption that the population level auxiliary information is available at primary stage unit level under two stage sampling design. population level auxiliary information is available at primary stage unit level under two stage sampling design. The variance and the estimator of the variance of the proposed estimators were also developed. Theoretical results obtained are demonstrated through simulation studies. Empirical results show that the proposed estimators outperforms the usual regression estimators under two stage sampling design in terms of the criteria of relative bias and relative root mean square error. Keywords: Auxiliary information, Calibration approach, Regression type estimator, Primary stage unit, Two stage sampling.

  In the process of phenotypic evaluation of sugarcane, many traits are simultaneously evaluated. These traits are often highly interrelated; evaluation of all these traits is costly and may not enhance selection response. In this study, we used the Principal Component Analysis (PCA) to identify representative traits for phenotypic characterization of sugarcane, and thereby to select superior clones in the breeding process. The results indicate that when PCA is used, only 10 out of 17 traits are significant in identifying the superior clones and their contribution to the selected traits is quantified. Keywords: MANOVA, Multivariate analysis, Principal component analysis, Trait combination, Variable reduction.