In the present study, the class of nonlinear models, with intrinsically linearly related mean response and input variables, were explored for the generation of locally D-optimal designs. It has been found that these models have the advantage of design construction in transformed or coded design space with suitable transformation in initial parameter guesses. Exponential and Poisson regression models with two continuous input variables were investigated. For the construction of D-optimal designs, the modified version of Fedorov algorithm was used that require a suitable candidate set representing the design space along with the initial parameter guesses. The efficient method of constructing the candidate sets with respect to each model is proposed. The optimality of generated designs was validated using general equivalence theorem. Keywords: Candidate set, D-optimality, Fisher information matrix, General equivalence theorem, Modified Fedorov exchange algorithm, Standardized variance function.

  In the present paper, robustness of the model based estimator of finite population total under error-in-variables super population model in stratified sampling has been investigated. An empirical study with real data revealed that the stratified balance sampling has minimized the percent loss in precision to the great extent due to measurement error in y along with protection against the deviation of the assumed/working model. loss in precision to the great extent due to measurement error in y along with protection against the deviation of the assumed/working model. loss in precision to the great extent due to measurement error in y along with protection against the deviation of the assumed/working model. Keywords: Robustness, Super population model, Finite population, Stratified balance sampling, Measurement error.

  Regression analysis is a widely used technique for studying the relationship between variables. In this paper, an attempt has been made to study the estimation of regression coefficient in the context of two-stage survey data using single auxiliary variable. The theory of calibration approach is used to develop the estimators based on assumption that auxiliary information is available at primary stage unit (psu) level, and at both psu and second stage unit level. The expression for variance and variance estimator is obtained. The performance of the developed estimators is evaluated through a real data based simulation study. Keywords: Regression coefficient, Calibration approach, Two-stage sampling. Keywords: Regression coefficient, Calibration approach, Two-stage sampling.

  In the present paper, an attempt has been made to examine the effect of measurement error in the study variate on the efficiency of the modelbased estimators of finite population total under super population model when variance of the study variate, y, is a function of the auxiliary variable x, related to y, and included as an independent variable in the model. Simulation results show that there is considerable loss in the precision of the estimators due to measurement error. However, such losses are marginal if the variability in the measurement errors as compared to variability in model errors is small. Keywords: Super population model, Measurement errors, Regression, Finite population, Prediction.

  To draw inference from the collected data under the pilot study was taken up to examine the reliability of estimates of crop area and crop production at state and national level on the basis of sample sizes recommended by the Prof. Vaidyanathan committee report, a paper based survey was conducted in five states, namely, Assam, Uttar Pradesh, Karnataka, Gujarat and Odisha. Under this survey in all this 5 states all the districts were enumerated with total sample size of 4700 villages and to digitize the huge data collected under this project, there is a need to develop a data entry software for digitization of the primary data. This paper deals with the development and implementation of the data entry software under this project. To make the software user friendly the software was developed using .Net Technology. The software was implemented in all the 5 states under the survey and it was found performing properly. Keywords: Data entry software; .Net technology; Pilot study; Sample size; Vaidyanathan committee.

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  Cointegration analysis and the existence of unit root often suggest an economic relationship in the long run for more than one non stationary time series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We series. In this paper, a unit root process and cointegration model of first order for processes which allows for logistic innovation is defined. We propose the maximum likelihood estimator of the cointegrating vector from a first order vector autoregressive process. Then we develop a likelihood ratio test for unit root and cointegration associated with two time series. Monte Carlo simulations are performed to verify the finite sample properties of the estimator and the test statistic. To account for the distortions caused by the specific sample, a bootstrap test based on MLE is performed. Rubber consumption and export data are analysed to illustrate the applications of the proposed model. AMS classifications : 91G70, 91B84, 37M10. Keywords: Bootstrapping, Cointegration, Maximum likelihood estimation, Unit root.

  Long range dependency or long run persistence is a common issue in agricultural price data. These type of phenomena in time-series process can be modeled with the help of Autoregressive fractionally integrated moving average (ARFIMA) model. The feature often arises when working with real time-series data which might exhibit long memory is the possible presence of structural break in mean or in long memory parameter. In this study, the statistical tests for testing presence of long memory and structural break have been discussed. The joint test (Gil-Alana, 2002) for testing degree of fractional integration and possible presence of structural break at known time epoch is also discussed. Two stage forecasting (TSF) algorithm by Papailias and Dias (2015)is used to obtain the forecasts of a long memory process in presence of structural break. In the present investigation, TSF approach is considered for forecasting daily wholesale price of pigeon pea in Bhopal market of Madhya Pradesh, India. A comparative study of predictive performances has also been carried out among the existing forecasting methodology of a long memory time-series subjected to structural break viz. AR approximation method and AR truncation method. It is concluded that TSF approach outperforms the other methods as far as forecasting is concerned for the series under consideration. Keywords: ARFIMA, Long memory, Pigeon pea, Structural break, Two stage forecasting.

  Research in the field of agriculture is increasing in such a way that it is getting very tedious job for the scholars to find out their intended research paper by accessing the journals available in our library. Document categorization, in the field of machine learning, is a field of study by which the job of classification does not need any human intervention. The task of classification is done automatically by the machine itself. In this work, a number of research titles has been classified using various machine learning algorithms for searching the best classifying algorithm for document categorization. Keywords : Text categorization, Text mining, Machine learning, Receiver operating characteristic, Entropy, Classifiers, Data mining techniques, Learning algorithms.

  Careful choice of parental lines and efficient mating designs form the backbone to a successful plant breeding programme. Objectives of the study, nature of genotypes, pollination type, space, cost, heterogeneity present in the field, wind direction, etc. are some of the deciding factors of a mating design. For wind pollinated species, a group of selected genotypes are to be arranged in isolated blocks/rows and columns such that each genotype gets an equal chance of pollinating, or being pollinated by, any of the others. Different types of designs for polycross trials are conducted for different situations like octa neighbour balanced polycross designs, designs for directional wind system, neighbour restricted polycross designs, etc. For ready referencing and potential use of these designs, online software for generation of these designs is highly desirable. In this paper, the development of a web solution for generation of different classes of polycross designs based on client?server architecture has been discussed. The software webPD is easily accessible at any time from any arbitrary platform throughout the globe through the use of internet. This software provides freely available solution for the researchers, breeders and students working in this area. Keywords: Polycross designs, Neighbour restricted designs, Octa neighbour balanced designs, Directional wind system, Web solution, Online catalogue.

  The objective of Handwritten Optical Character Recognition (HOCR) is automatic reading of optically intellect document text materials to translate human-readable characters to machine- readable codes. In Optical Character Recognition, the text lines in a document must be segmented properly before recognition. English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. This is the motivation behind choosing OCR for Odia language. An odia handwritten paragraph is chosen for processing and recognition. The HOCR system is devised to first segment the whole document into text lines, then to words and then to individual characters. These characters are then used to extract the necessary features and recognize those characters and classify them. Keywords: Optical Character Recognition (OCR), Handwritten Odia Character Recognition (HOCR), Template matching.