Pearson's correlation is one of the most common measures of linear dependence. Recently, Bernardo (11th International Workshop on Objective Bayes Methodology, 2015) introduced a flexible class of priors to study this measure in a Bayesian setting. For this large class of priors, we show that the (marginal) posterior for Pearson's correlation coefficient and all of the posterior moments are anal...

The field experiment was carried out during Rabi season of the year 2011-2012 on the experimental field of Department of Horticulture, Dr. PDKV, Akola, Maharashtra (India). The study was undertaken on twenty four genotypes of coriander using Randomized Block Design with three replications. This study revealed that number of umbels per plant (0.25) and test weight (0.31) was associated significa...

\Ve propose a new method for robust image registrat,ion called 'Selective Correlation Coefficient (SCC)' in order to search images under illconditioned illuminat,ion or partial occlusion. A correlation mask image is generated for selecting pixels of a image before matching. The mask image can be derived from a binary-coded increment sign image defined from any object image and the template imag...

Many statistical analyses can be undertaken to examine the relationship between two continuous variables within a group of subjects. Two of the main purposes of such analyses are: To assess whether the two variables are associated. There is no distinction between the two variables and no causation is implied, simply association. To enable the value of one variable to be predicted from any known...

Inference concerning the correlation coefficient of two random variables from the bivariate normal distribution has been investigated by many authors. In particular, Fisher [1915. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika 10, 507–521] and Hotelling [1953. New light on the correlation coefficient and its trans...

The concordance correlation coefficient is commonly used to assess agreement between two raters or two methods of measuring a response when the data are measured on a continuous scale. However, the situation may arise in which repeated measurements are taken for each rater or method, e.g. longitudinal studies in clinical trials or bioassay data with subsamples. This paper proposes a coefficient...

Even when a healthy individual is studied, his/her erythrocytes in capillaries continually change their shape in a synchronized erratic fashion. In this work, the problem of characterizing the cell behavior is studied from the perspective of bounded correlated random walk, based on the assumption that diffractometric data involves both deterministic and stochastic components. The photometric re...

Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values....

Recently, Fullér et al. [Fuzzy Sets and Systems, 2010] introduced a new f -weighted index of interactivity, ρf (A,B), between marginal possibility distributions A and B of a joint possibility distribution C for any weighting function f . They also left two open questions in connection with the lower limit for f -weighted possibilistic correlation coefficient related to the improved index of int...

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. In this paper we analyze three different recommendation generation algorithms. We look into three different techniques for computing similarities for obtaining recommendations from them. On the basis of various parameters...