Special investigation of the Monona County Engineer’s Office for the period January 1, 2004 through April 20, 2007

Special investigation of the Monona County Engineer’s Office for the period January 1, 2004 through April 20, 2007

Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size

This paper analyzes whether standard covariance matrix tests work whendimensionality is large, and in particular larger than sample size. Inthe latter case, the singularity of the sample covariance matrix makeslikelihood ratio tests degenerate, but other tests based on quadraticforms of sample covariance matrix eigenvalues remain well-defined. Westudy the consistency property and limiting distribution of these testsas dimensionality and sample size go to infinity together, with theirratio converging to a finite non-zero limit. We find that the existingtest for sphericity is robust against high dimensionality, but not thetest for equality of the covariance matrix to a given matrix. For thelatter test, we develop a new correction to the existing test statisticthat makes it robust against high dimensionality.

Iowa Lottery Action Special Edition, Dec. 3, 2007

Special edition newsletter for Iowa lottery retailers.

Special investigation of the City of Rathbun for the period July 1, 2003 through November 30, 2007

Special investigation of the City of Rathbun for the period July 1, 2003 through November 30, 2007

Honey, I shrunk the sample covariance matrix

The central message of this paper is that nobody should be using the samplecovariance matrix for the purpose of portfolio optimization. It containsestimation error of the kind most likely to perturb a mean-varianceoptimizer. In its place, we suggest using the matrix obtained from thesample covariance matrix through a transformation called shrinkage. Thistends to pull the most extreme coefficients towards more central values,thereby systematically reducing estimation error where it matters most.Statistically, the challenge is to know the optimal shrinkage intensity,and we give the formula for that. Without changing any other step in theportfolio optimization process, we show on actual stock market data thatshrinkage reduces tracking error relative to a benchmark index, andsubstantially increases the realized information ratio of the activeportfolio manager.

Special investigation of Hardin County Solid Waste Disposal Commission for the period January 1, 2002 through December 31, 2007

Special investigation of Hardin County Solid Waste Disposal Commission for the period January 1, 2002 through December 31, 2007

Special investigation of the City of Postville for the period July 1, 2000 through June 30, 2006

Special investigation of the City of Postville for the period July 1, 2000 through June 30, 2006

Special investigation of the Area XV Regional Planning Commission located in Ottumwa, Iowa for the period July 1, 2000 through June 30, 2006

Special investigation of the Area XV Regional Planning Commission located in Ottumwa, Iowa for the period July 1, 2000 through June 30, 2006

Special investigation of selected accounts at Burlington High School for the period July 1, 2004 through February 16, 2006

Special investigation of selected accounts at Burlington High School for the period July 1, 2004 through February 16, 2006

Special investigation of the City of Mingo for the period September 1, 2002 through June 30, 2006

Special investigation of the City of Mingo for the period September 1, 2002 through June 30, 2006

An adaptation of correspondence analysis for square tables

The application of correspondence analysis to square asymmetrictables is often unsuccessful because of the strong role played by thediagonal entries of the matrix, obscuring the data off the diagonal. A simplemodification of the centering of the matrix, coupled with the correspondingchange in row and column masses and row and column metrics, allows the tableto be decomposed into symmetric and skew--symmetric components, which canthen be analyzed separately. The symmetric and skew--symmetric analyses canbe performed using a simple correspondence analysis program if the data areset up in a special block format.

Improved estimation of the covariance matrix of stock returns with an application to portofolio selection

This paper proposes to estimate the covariance matrix of stock returnsby an optimally weighted average of two existing estimators: the samplecovariance matrix and single-index covariance matrix. This method isgenerally known as shrinkage, and it is standard in decision theory andin empirical Bayesian statistics. Our shrinkage estimator can be seenas a way to account for extra-market covariance without having to specifyan arbitrary multi-factor structure. For NYSE and AMEX stock returns from1972 to 1995, it can be used to select portfolios with significantly lowerout-of-sample variance than a set of existing estimators, includingmulti-factor models.

Special investigation of the City of Center Point Library for the period January 1, 2006 through December 6, 2007

Special investigation of the City of Center Point Library for the period January 1, 2006 through December 6, 2007

Special investigation of the City of Schleswig for the period January 1, 2003 through January 31, 2007

Special investigation of the City of Schleswig for the period January 1, 2003 through January 31, 2007

Computation of multiple correspondence analysis, with code in R

The generalization of simple correspondence analysis, for two categorical variables, to multiple correspondence analysis where they may be three or more variables, is not straighforward, both from a mathematical and computational point of view. In this paper we detail the exact computational steps involved in performing a multiple correspondence analysis, including the special aspects of adjusting the principal inertias to correct the percentages of inertia, supplementary points and subset analysis. Furthermore, we give the algorithm for joint correspondence analysis where the cross-tabulations of all unique pairs of variables are analysed jointly. The code in the R language for every step of the computations is given, as well as the results of each computation.