Pranab Kumar Sen

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We present a general methodology for performing statistical inference on the components of a real-valued matrix parameter for which rows and columns are subject to order restrictions. The proposed estimation procedure is based on an iterative algorithm developed by Dykstra and Robertson (1982) for simple order restriction on rows and columns of a matrix.(More)
Given a p-dimensional random variable yell and a q-dimensional random variable-y(2) , the first canonical correlation leads to finding Q* E Rp and (3* E R q which maximizes the correlation between Q'y(l) and (3'y(2). However, in many practical situations (e.g. educational testing problems, neural networks), some natural restrictions on the coefficients Q(More)
The classical distribution theory of the log likelihood ratio test statistic does not hold for testing homogeneity (i.e., no mixture) against mixture alternatives. Asymptotic theory for this problem is developed. For some special cases, asymptotically locally minimax tests are also found. It is pointed out that the main problem is lack of identifiability of(More)
Transferrin (TF) and lactoferrin (LF) are probably the major sources of iron (Fe) for Neisseria gonorrhoeae in vivo. We isolated mutants of N. gonorrhoeae FA19 that were unable to grow with Fe bound to either TF (TF-) or LF (LF-) or to both TF and LF ([TF LF]-). The amount of Fe internalized by each of the mutants was reduced to background levels from the(More)
In complex diversity analysis, specially arising in genetics, genomics, ecology and other high-dimensional (and sometimes low sample size) data models, typically subgroup-decomposability (analogous to ANOVA decomposability) arises. In groupdivergence of diversity measures in a high-dimension low sample size scenario, it is shown that Hamming distance-type(More)
In repeated measures settings, modeling the correlation pattern of the data can be immensely important for proper analyses. Accurate inference requires proper choice of the correlation model. Optimal efficiency of the estimation procedure demands a parsimonious parameterization of the correlation structure, with sufficient sensitivity to detect the range of(More)
Estimating the correlation coefficient between two outcome variables is one of the most important aspects of epidemiological and clinical research. A simple Pearson's correlation coefficient method is usually employed when there are complete independent data points for both outcome variables. However, researchers often deal with correlated observations in a(More)