We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the purposes for which the simulations are used. We recommend applying a battery of tests for mixing based on the comparison of inferences from individual sequences and from the mixture of sequences. Finally, we discuss multivariate analogues, for assessing convergence of several parameters simultaneously.

/pdf/general-methods-for-monitoring-convergence-of-iterative-45g63vyekv.pdf

General methods for monitoring convergence of iterative simulations

http://www.geog.ucl.ac.uk/~mdisney/teaching/PPRS/papers/foody.pdf

Status of land cover classification accuracy assessment

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.In this article, we show using two simple attacks that a k-anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks, and we propose a novel and powerful privacy criterion called e-diversity that can defend against such attacks. In addition to building a formal foundation for e-diversity, we show in an experimental evaluation that e-diversity is practical and can be implemented efficiently.

/pdf/l-diversity-privacy-beyond-k-anonymity-54qdj1526d.pdf

L-diversity: Privacy beyond k-anonymity

On robust estimation of the location parameter

The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).

Chapter 28 How to Write Up and Report PLS Analyses

Improving on the positive part of the UMVUE of a noncentrality parameter of a noncentral chi-square distribution

An extended class of minimax generalized Bayes estimators of regression coefficients

A unified and generalized set of shrinkage bounds on minimax Stein estimates

The usual method of combining sample data with auxiliary information is the familiar precision-weighted composite estimator. However, application of this estimator is straightforward only when the auxiliary information is unbiased. If this is not the case, and the bias is unaccounted for, then the risk of the usual composite estimator can be greater than that of the sample mean. We conjecture that investigators are often unsure of the possible bias in their auxiliary information (...)

Combining inventory estimates with possibly biased auxiliary information

For the canonical problem of estimating the mean of a multivariate normal distribution with a known covariance matrix using a squared error loss, we give a general method for finding estimates that have risk functions identical to that of a given inadmissible estimate In the case of more than one dimension, the estimates considered are spherically symmetric, but in one dimension no such assumption is made Generally speaking, we characterize all estimates which have the risk duplication property It is proven that every James-Stein estimator except $(1 - (p - 2)/ \parallel X \parallel^2) \rm X$ can be duplicated in risk by infinitely many estimators A general theorem is presented from which a principal inadmissibility result for spherically symmetric estimates in Brown follows This and the other results all basically depend on solving Riccati differential equations of an appropriate kind A curious result is that two smooth estimates whose graphs intersect cannot have identical risk Several results for the entire discrete exponential family and the binomial case demonstrate that the phenomena are fundamentally different in continuous and discrete cases Our results indicate a new method for constructing explicit dominating estimates that may work in many problems

/pdf/all-estimates-with-a-given-risk-riccati-differential-2i51ua8kgt.pdf

William E. Strawderman

Papers

Improving on the positive part of the UMVUE of a noncentrality parameter of a noncentral chi-square distribution

An extended class of minimax generalized Bayes estimators of regression coefficients

A unified and generalized set of shrinkage bounds on minimax Stein estimates

Combining inventory estimates with possibly biased auxiliary information

All estimates with a given risk, Riccati differential equations and a new proof of a theorem of Brown