1982 Technical Reports
For full reports, please contact Statistics Department at (765) 494-6030.-
B. Davis. On Brownian slow points.
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S. S. Gupta, D. Y. Huang, C. L. Chang. Selection procedures for optimal subsets of regression variables. This technical report is no longer available. Please see 83-29.
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G. P. McCabe. Principal variables.
Published in Technometrics, Vol. 26, pp. 137-144, 1984.
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A. Zaman. Urn models for Markov exchangeability.
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A. Zaman. A non-clustering property of stationary sequences.
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S. S. Gupta, K. J. Miescke. Sequential selection procedures - a decision theoretic approach.
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S. S. Gupta, H.-M. Yang. On subset selection procedures for inverse Gaussian populations.
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M. C. Spruill. A method for the determination of optimal model robust designs in the estimation of a linear functional.
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J. O. Berger. The robust Bayesian viewpoint.
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K.-C. Li. Regression Models with Infinitely Many Parameters: Consistency of Bounded Linear Functionals.
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P. K. Goel, A. G. Rocco. ARIMA modeling and forecasting: An interactive program based on IMSL subroutine package-I.
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P. K. Goel, A. G. Rocco. ARIMA modeling and forecasting: An interactive program based on IMSL subroutine package-II.
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V. L. Anderson, R. A. McLean. Design of experiments and statistical analysis of biological studies.
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V. L. Anderson, R. A. McLean.Design of experiments for industrial engineers.
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P. F. Campbell, G. P. McCabe. Factors relating to persistence in a computer science major. Published in Communications of the ACM, Vol. 27, pp. 1108-1113, 1984.
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W. Y. Wong. On the property of dullness of Pareto distribution.
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S. M. Samuels. Exact solutions for the full information best choice problem.
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S. S. Gupta, S. Panchapakesan. Edgeworth expansions in Statistics: Some Recent Developments.
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C. Spruill. Bounds on the Performance of Spherically Symmetric Estimators Which Dominate the James-Stein Estimator.
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A. Mattenklott, K. Miescke, J. Sehr. A stochastic model for paired comparisons of social stimuli.
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L. J. Gleser, D. S. Moore. The effect of dependence on chi-squared and empiric distribution tests of fit.
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S. S. Gupta, W. T. Huang. On isotonic selection rules for binomial populations better than a standard.
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L. J. Gleser. Confidence regions for the slope in a linear errors-in-variables regression model. J. Chen, H. Rubin. A Note on the Behavior of Sample Statistics When the Population Mean is Infinite.
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K. J. Miescke. Recent results on multi-stage selection procedures.
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A. L. Rukhin. Adaptive tests in statistical problems with finite nuisance parameter.
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S. S. Gupta, W. C. Kim. A two-stage elimination type procedure for selecting the largest of several normal means with a common unknown variance.
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P. S. Puri, E. S. Tollar. On the limit behavior of certain quantities in a subcritical storage model.
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M. E. Bock, P. Klembeck. Minimal Estimators Incorporating Vague Prior Knowledge in Spherically Symmetric Location Problems.
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D. Y. Huang, S. Panchapakesan and S. T. Tseng. Some Locally Optimal Subset Selection Rules for Comparison with a Control.
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D. Dey, J. Berger. On truncation of shrinkage estimators in simultaneous estimation of normal means.
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S. Hui, J. Berger. Empirical Bayes estimation of rates in longitudinal studies.
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J. Berger, R. Wolpert. The Likelihood Principle: A Review and Generalizations. (No longer available as a tech report at Purdue. This paper has been published as an IMS Monograph.)
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A. L. Rukhin. Adaptive Classification Procedures.
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This number was never given out.
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D. Y. Huang, S. T. Tseng. Gamma-optimal decision procedures for selecting the best population in randomized complete block design.
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S. S. Gupta, D. Y. Huang, S. Panchapakesan. On some inequalities and monotonicity properties with special reference to selection and ranking problems.
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K.-C. Li, J. T. Hwang. The data-smoothing aspect of Stein estimates.
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J. Berger. Bayesian Salesmanship.
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B. Davis. On the paths of symmetric stable processes.
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K.-C. Li. Consistency for Cross-validated Nearest Neighbor Estimates in Nonparametric Regression. B. Flury, H. Riedwyl. T2-test, linear two-group discriminant analysis, and their computation by linear regression