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In this article, we study the geometric distribution under randomly censored data. Maximum likelihood estimators and confidence intervals based on Fisher information matrix are derived for the unknown parameters with randomly censored data. Bayes estimators are also developed using beta priors under generalized entropy and LINEX loss functions. Also, Bayesian credible and highest posterior density (HPD) credible intervals are obtained for the parameters. Expected time on test and reliability characteristics are also analyzed in this article. To compare various estimates developed in the article, a Monte Carlo simulation study is carried out. Finally, for illustration purpose, a randomly censored real data set is discussed.

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Genetic diversity and the association of the target traits with other traits are the foundation for any crop improvement programme. 308 genotypes of spring wheat reference set (SWRS) were evaluated in simple lattice during crop-season 2015-16 to study genetic diversity and, character association. Phenotypic data were recorded on 15 nutrients and grain yield traits. Analysis of variance revealed significant differences among all the genotypes. The estimates of CV indicated highest variability for agronomic traits like GWPS followed in descending order by GYPP, TNPP, TGW, GNPS and biological yield. For nutrient traits, the maximum variability was observed for GZnC, followed in decreasing order by GBCC, GFeC and GPC. Correlation was calculated among all the 15 traits. Out of the total 105 paired combinations of all the 15 traits, only 72 combinations were significant (36 positive and 36 negative). The values of significant positive correlation varied from 0.11 (GPC-Fe) to 0.99 (DTH-DTA) and values of significant negative correlation ranged from-0.11 (TNPP-GYPP) to-0.724 (DTA-GYPP) respectively. Genetic diversity analysis formed 12 groups and intra-cluster distance varied from 31.97 to 163.55 with the maximum distance was in cluster-II and the minimum distance was in cluster-X, indicated close relationships among the genotypes. The highest inter-cluster distance was exhibited between clusters I and II (D 2 = 696.93), indicated a wider genetic divergence among the clusters. The lowest D 2 value was observed between clusters X and XI (D 2 = 44.47), indicated that these clusters were genetically close. Further we selected eight important clusters for different traits, out of which cluster III was important for GPC and GZnC, cluster V was for iron and cluster VI was for DTM, TGW and GBCC respectively, remaining five clusters were for remaining agronomic trait. Furthermore, we also selected 10 common genotypes with highest trait values for different traits, these genotypes may be further used for improvement of more than one traits with high trait value in wheat breeding programe.

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