The current course of human development, along with its driving forces such as globalization and urbanization, appears to be rapid and inevitable. Hence ensuring bio-security against emerging and re-emerging diseases is among the top challenges in this day and age. The present paper will discuss some of the projects that are being conducted internationally towards realizing the aim of ensuring bio-security through a variety of research approaches, many of which were developed in the past decade. In particular, we focus on the research methodology developed by the authors to enable parametric modeling of immunologic profiles of individual subjects, which can be further extended to large groups and populations. Keywords: Bio-security, Population immuno-phenome, Classification, Skew mixture models, Clustering single cells.

  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 model-based 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.

  Long-term annual, seasonal and monthly trends in rainfall at 30 sub-divisional meteorological stations and temperature (maximum and minimum) in seven homogenous regional scales in India were investigated for trend analysis. The monthly (January?December), seasonal (winter, summer, monsoon/rainy and post-monsoon seasons) and annual rainfall series for each of the 30 meteorological sub-divisions along with monthly temperature series for all-India and seven homogeneous regions, viz., Western Himalaya (WH), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Coast (WC), East Coast (EC) and Interior Peninsula (IP) were procured from Indian Institute of Tropical Meteorology, Pune, India (IITM: htpp://www.tropmet.res.in). Modified Mann-Kendall test (if the time series data are not serially independent), Sen?s slope estimator and linear regression approaches were utilised for assessing the statistical significance of trend and variability in meteorological data. There are no significant trends in monthly rainfall at most of the synoptic stations in India. However, the maximum number of stations with negative trends have been observed in December (21 stations), and then in September (19 stations) and January (16 stations) and with positive trends in April (26 stations) and October (25 stations). For annual rainfall, 15 sub-divisional meteorological stations showed decreasing trends. Significant trends in annual rainfall have been noticed only at three stations (East Madhya Pradesh, Konkan and Goa and Coastal Karnataka) only. For seasonal trends, 20 sub-divisional meteorological stations showed decreasing trend in winter season (January and February). Nine sub-divisional meteorological stations showed decreasing trend in summer season (March, April and May), 16 sub-divisional meteorological stations showed decreasing trend in monsoon season (June, July, August, September) while eight sub-divisional meteorological stations showed decreasing trend in post-monsoon season (October, November and December). Significant trends in seasonal rainfall have been noticed in one sub-divisional meteorological station in winter season, three sub-divisional meteorological stations in summer, six sub-divisional meteorological stations in monsoon season and one sub-divisional meteorological station in post-monsoon. There was significant rising trend in maximum temperature in most of the months while minimum temperature in various regions showed increasing trends; but it had low significance level as compared to maximum temperature. Keywords: Trend analysis, Rainfall, Temperature, Non-parametric test, Modified Mann?Kendall test, Sen?s slope estimator.

  In many practical situations the linear model is not complex enough to capture the underlying relationship between the response variable and its regressors. This paper explores this association in dairy cattle data using the partially linear single- index survival model (PLSISM). In addition, parametric accelerated failure time (AFT) survival models were also used. Calf survival and culling data (survival from first calving to second calving) sets were used. The calf survival data contains, as covariates, arrival weight, weaning weight, total serum protein, calving ease score, herd-year-season of calving and number of disease incidences. The culling data set includes: age at first calving, body condition score, level of production (milk, fat and protein yield), herd size variation, type of milk recording and herd-year-season of calving. For the calf survival data, arrival body weight, weaning weight, total serum protein and age at first calving were included in the nonparametric component of the PLSISM. For the culling data, body condition score and fat production were included in the nonparametric component of the PLSISM. All factors included in the respective models of the two data sets had a statistically significant effect on the survival time. The simpler AFT model provides an intuitively more simple interpretation of covariate effects. Indeed, estimates of the parametric component were similar for the two models of the two data sets. However, the estimates of the nonparametric component differed from the parametric analysis. This difference may be attributed largely to the nonlinearity of the estimated function, suggesting that the standard linear model did not adequately capture the underlying association between the response and regressors in this study. Keywords: Survival, Nonlinear, Accelerated failure time model, Dairy cattle.

  In this paper reviewed some work of successive sampling by various authors. The work done so far in successive sampling is known technique that can be used in regression, ratio, super-population model and longitudinal surveys to estimate population parameters and measurements of difference or change of a study variable. Some regression type estimators of population mean have been proposed for successive sampling using information of two or more auxiliary variables. This class includes a number of regression estimators (Sen 1971, 1972 and 1973) and also the class of estimators suggested by Singh et al. (2008, 2009, 2010, and 2012). Keywords: Auxiliary variable, Longitudinal surveys, Ratio estimator, Regression estimator, Successive sampling, Super-population model.

  We provide an approach alternative to extreme vertices designs when there are constraints on the components of the mixture. The approach is based on the idea that given a suitably chosen base design for the unconstrained simplex, one can find another design within the constrained feasible region, which is closest to the base design in some meaningful sense. The problem is formulated as a quadratic program. These designs are able to avoid certain undesirable features of extreme vertices designs such as exclusive reliance on constraints to identify the design, not knowing a priori the number of vertices and the number of centroids and therefore the overall size of the design and in case, a fixed design size is already specified, issues faced pertaining to which of the centroids should be included in the final design. Several examples illustrating these issues and corresponding solutions are presented. Keywords: Bounds, Extreme vertices designs, Interior mixture designs, Mixture experiments, Procrustation, Quadratic programming.

  A class of estimators for ratio of two population mean of study variables using the knowledge of population mean of an auxiliary variable is proposed, its bias and mean square error are found. A sub-class of optimum estimators in the sense of having minimum mean square error is found and enhancing the practical utility, a sub-class of estimators depending on estimated optimum value based on sample observations is also investigated in the presence of non-response. Keywords: A class of estimators, Auxiliary information, Non-response, Bias, Mean square error and efficiency.

  In this paper a compromise allocation is obtained in multivariate stratified sampling survey. The problem has been formulated as a multiobjective nonlinear programming problem and approximated to a linear programming problem (LPP) using two separate methods (i) Piecewise linear approximation method and (ii) Taylor series approximation. The solutions of both the LPP have been obtained through fuzzy goal programming. A numerical example is also provided to show the applicability of the methods. A comparison of the approaches has also been made. Keywords: Compromise allocation, Coefficient of variation, Multivariate stratified sampling, Fuzzy goal programming, Piecewise linear approximation.

  In plant breeding programmes, Mating-Environmental (ME) designs are commonly used under one-way blocking setup to get an estimate for general and specific combining abilities (gca and sca) of inbred lines involved in the crosses. When a large number of crosses are to be compared, which lead to a large experimental area, it may be important to account for or eliminate the effects of fertility trends in the land in two directions. In such situations, designs with crosses arranged in a row-column (RC) set up can be more advantageously used. Though information on both gca and sca effects are important to the breeders, most of the statistical papers dealing with these designs assume sca effects as negligible for reducing the complexity in mathematical derivations. In this paper, a linear fixed effects model under a row-column setup with gca and sca components has been defined and the information matrix for estimating gca effects free from sca effects has been derived. Further, some classes of efficient MERC designs have been obtained for complete diallel cross (CDC) experiments and the designs obtained are found to be variance balanced for estimating the contrasts pertaining to gca effects. Macros have been developed using PROC IML of SAS software for the generation of the MERC designs so constructed. Keywords: Diallel cross, General combining ability effects, Specific combining ability effects, Row-column design, MERC designs.

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