J.N. Srivastava

Bio: J.N. Srivastava is an academic researcher from Colorado State University. The author has contributed to research in topics: Fractional factorial design & Linear model. The author has an hindex of 16, co-authored 38 publications receiving 1307 citations. Previous affiliations of J.N. Srivastava include Federal University of Rio de Janeiro.

A Survey of combinatorial theory

Abstract: : The textbook contains survey articles and research papers covering almost all areas of combinatorial mathematics, in particular graph theory, finite geometries, block designs, factorial designs, coding theory, number theory, combinatorial geometries, search theory, communication and computer science problems and combinatorial problems in statistical inference.

Multivariate Analysis, Design of Experiments, and Survey Sampling

Abstract: Sampling designs and prediction methods for Gaussian spatial processes design techniques for probabilistic sampling of items with variable monetary value small area estimation -a Bayesian perspective Bayes sampling designs for selection procedures cluster coordinated composites of diverse datasets on several spatial scales for designing extensive environmental sample surveys - prospectus on promising protocols corrected confidence sets for sequentially designed experiments, II -examples resampling marked point processes graphical Markov models in multivariate analysis robust regression with censored and truncated data multivariate calibration some consequences of random effects in multivariate survival models a unified methodology for constructing multivariate autoregressive models statistical model evaluation and information criteria multivariate rank tests asymptotic expansions of the distribution of some test statistics for elliptical populations a review of variance estimators with extensions to multivariate nonparametric regression models on affine invariant sign and rank tests in one and two sample multivariate problems correspondence and component analysis growth curve models dealing with uncertainties in queues and networks of queues optimal Bayesian design for a logistic regression model - geometric and algebraic approaches structure of weighing mattrices of small order and weight.

Tests for Parameter Instability and Structural Change with Unknown Change Point.

Abstract: This paper considers tests for parameter instability and structural change with unknown change point. The results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures. The asymptotic distributions of the test statistics considered here are nonstandard because the change point parameter only appears under the alternative hypothesis and not under the null. The tests considered here are shown to have nontrivial asymptotic local power against all alternatives for which the parameters are nonconstant. The tests are found to perform quite well in a Monte Carlo experiment reported elsewhere. Copyright 1993 by The Econometric Society.

Generalized inverses: theory and applications

Abstract: * Glossary of notation * Introduction * Preliminaries * Existence and Construction of Generalized Inverses * Linear Systems and Characterization of Generalized Inverses * Minimal Properties of Generalized Inverses * Spectral Generalized Inverses * Generalized Inverses of Partitioned Matrices * A Spectral Theory for Rectangular Matrices * Computational Aspects of Generalized Inverses * Miscellaneous Applications * Generalized Inverses of Linear Operators between Hilbert Spaces * Appendix A: The Moore of the Moore-Penrose Inverse * Bibliography * Subject Index * Author Index

Detection of influential observation in linear regression

Abstract: A new measure based on confidence ellipsoids is developed for judging the contribution of each data point to the determination of the least squares estimate of the parameter vector in full rank linear regression models. It is shown that the measure combines information from the studentized residuals and the variances of the residuals and predicted values. Two examples are presented.

Bayesian Experimental Design: A Review

Abstract: This paper reviews the literature on Bayesian experimental design. A unified view of this topic is presented, based on a decision-theoretic approach. This framework casts criteria from the Bayesian literature of design as part of a single coherent approach. The decision-theoretic structure incorporates both linear and nonlinear design problems and it suggests possible new directions to the experimental design problem, motivated by the use of new utility functions. We show that, in some special cases of linear design problems, Bayesian solutions change in a sensible way when the prior distribution and the utility function are modified to allow for the specific structure of the experiment. The decision-theoretic approach also gives a mathematical justification for selecting the appropriate optimality criterion.

Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies

Abstract: When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in the GP model, and A-, D-, or E-optimal design. In this paper, we tackle the combinatorial optimization problem of maximizing the mutual information between the chosen locations and the locations which are not selected. We prove that the problem of finding the configuration that maximizes mutual information is NP-complete. To address this issue, we describe a polynomial-time approximation that is within (1-1/e) of the optimum by exploiting the submodularity of mutual information. We also show how submodularity can be used to obtain online bounds, and design branch and bound search procedures. We then extend our algorithm to exploit lazy evaluations and local structure in the GP, yielding significant speedups. We also extend our approach to find placements which are robust against node failures and uncertainties in the model. These extensions are again associated with rigorous theoretical approximation guarantees, exploiting the submodularity of the objective function. We demonstrate the advantages of our approach towards optimizing mutual information in a very extensive empirical study on two real-world data sets.