Nicolas W. Hengartner

Quantitative analysis of literary styles

Writers are often viewed as having an inherent style that can serve as a literary fingerprint. By quantifying relevant features related to literary style, one may hope to classify written works and even attribute authorship to newly discovered texts. Beyond its intrinsic interest, the study of literary styles presents the opportunity to introduce and motivate many standard multivariate statistical techniques. Today the statistical analysis of literary styles is made much simpler by the wealth of real data readily available from the Internet. This article presents an overview and brief history of the analysis of literary styles. In addition we use canonical discriminant analyis and principal component analysis to identify structure in the data and distinguish authorship.

Environmental equity and the distribution of toxic release inventory and other environmentally undesirable sites in metropolitan New York City

We study the question of environmental equity via generalized linear modeling for the metropolitan New York City region and ask whether, after accounting for socioeconomic status, particular racial/ethnic populations bear a disproportionate burden of hosting environmentally undesirable sites. Our data consist of population demographics for 2216 census tracts linked to 354 environmentally undesirable facilities, including toxic release inventory sites, hazardous waste treatment, storage, and disposal facilities, and other common urban problem sites such as landfills, incinerators, bus garages and sewage treatment plants. Using generalized linear and additive modeling techniques, we find that racial/ethnic demographics, in particular the Hispanic percentage of a tracts population, are significantly associated with the presence of potentially environmentally adverse sites. This leads us to the conclusion that, over the whole metropolitan New York City area, the Hispanic population is proximate to more sites than other populations. At the same time, we find that both Hispanics and African-Americans are more proximate to these sites in the Bronx and Queens. However, we also find indications that Hispanics and African-Americans are less likely to be proximate to the sites in Manhattan. We establish an empirical relationship that warrants additional study in order to establish the causes for the population distribution and whether a basis for a claim of discrimination exists.

Bandwidth selection for local linear regression smoothers

Summary. The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.

Estimation and selection procedures in regression: An L1 approach

The authors consider the problem of estimating a regression function go involving several variables by the closest functional element of a prescribed class G that is closest to it in the L1 norm. They propose a new estimator ĝ based on independent observations and give explicit finite sample bounds for the L1distance between ĝg and go. They apply their estimation procedure to the problem of selecting the smoothing parameter in nonparametric regression.

A Note on Maximum Likelihood Estimation

Abstract An experimenter collects two independent samples of binary (zero-one) random variables: In the first sample, the probability of success is p; in the second sample, the probability of success is q < p. The question then arises over whether he should use both samples to estimate p. In this note it is shown that, depending on the probability of success p, the maximum likelihood estimator using both samples has a larger variance and mean squared error than the maximum likelihood estimator that uses only the first sample. This is in contradiction with the naive principle: the precision of the maximum likelihood estimate does not decrease when additional independent observations are used.

Nonparametric regression estimation at design poles and zeros

In most treatments of nonparametric regression, it is assumed that the marginal density of the explanatory variables is strictly bounded away from zero and infinity. This note investigates the pointwise asymptotics for nonparametric regression when this assumption fails, that is, the marginal density of the explanatory variable has either an isolated zero or a pole at the point of interest.