Key Statistic Research Articles

A Labor Capital Asset Pricing Model

We show that labor search frictions are an important determinant of the cross-section of equity returns. In the data, sorting firms by loadings on labor market tightness, the key statistic of search models, generates a spread in future returns of 6% annually. We propose a partial equilibrium labor market model in which heterogeneous firms make optimal employment decisions under labor search frictions. In the model, loadings on labor market tightness proxy for priced time variation in the efficiency of the matching technology. Firms with low loadings are not hedged against adverse matching efficiency shocks and require higher expected stock returns.

A Tractable Analysis of Contagious Equilibria

This paper studies contagious equilibrium in infinitely repeated matching games. The innovation is to identify a key statistic of contagious punishment that, used together with a recursive formulation, generates tractable closed-form expressions for continuation payoffs, off equilibrium. This allows a transparent characterization of the dynamic incentives created by contagious punishment schemes.

High school grades and university performance: A case study

A critical issue facing a number of colleges and universities is how to allocate first year places to incoming students. The decision to admit students is often based on a number of factors, but a key statistic is a student's high school grades. This paper reports on a case study of the subsequent performance at the University of Winnipeg of high school students from 84 Manitoba high schools. By tracking the university performance of students admitted for the years 1997–2002, we are able to estimate the likelihood of success of subsequent students based on their characteristics as well as their high school grades. In doing so, we use a number of alternative estimators including a Least Squares Dummy Variable Model and a Hierarchical Linear Model. The methodology should be of interest to admissions officers at other universities as an input into estimating the subsequent performance of first year students.

Trends analysis of impact factors for nuclear medicine journals

Despite debate as to its usefulness, the journal impact factor remains the key statistic gleaned from the journal citation report. An analysis of trends in nuclear medicine impact factors is yet to be reported in the literature. Moreover, the relationship between impacts factors and the other indices contained in the journal citation report are complex and seldom considered in interpreting the implication of impact factors in decision making. The 2006 journal citation report showed that nuclear medicine journals have demonstrated a trend towards increased impact factors. The J Nucl Med has continued its dominance as the most highly ranked nuclear medicine journal.

Lagrangian conditional statistics, acceleration and local relative motion in numerically simulated isotropic turbulence

Lagrangian statistics of fluid-particle velocity and acceleration conditioned on fluctuations of dissipation, enstrophy and pseudo-dissipation representing different characteristics of local relative motion are extracted from a direct numerical simulation database of stationary (forced) homogeneous isotropic turbulence. The grid resolution in the simulations is up to 20483, and the Taylor-scale Reynolds number ranges from about 40 to 650, where characteristics of small-scale intermittency in the Eulerian flow field are well developed. A key joint statistic of the conditioning variables is the dissipation-enstrophy cross-correlation, which is asymmetric, but becomes less so at high Reynolds number. Conditional velocity autocorrelations are consistent with rapid changes in the velocity of fluid particles moving in regions of large velocity gradients. Examination of statistics conditioned upon enstrophy, especially in a local coordinate frame moving with the vorticity vector, and of the centripetal acceleration suggests the presence of vortex-trapping effects which persist for several Kolmogorov time scales. Further results on acceleration statistics and joint velocity-acceleration autocorrelations are also presented to help characterize in detail the properties of a joint stochastic process of velocity, acceleration and the pseudo-dissipation. Together with recent work on Eulerian conditional acceleration and Reynolds-number dependence of basic Lagrangian quantities, the present results are directly useful for the development of a new stochastic model formulated to account for intermittency and Reynolds-number effects as described in detail in a companion paper.

Parametric vs. Non-Parametric Statistics of Low Resolution Electromagnetic Tomography (LORETA)

This study compared the relative statistical sensitivity of non-parametric and parametric statistics of 3-dimensional current sources as estimated by the EEG inverse solution Low Resolution Electromagnetic Tomography (LORETA). One would expect approximately 5% false positives (classification of a normal as abnormal) at the P < .025 level of probability (two tailed test) and approximately 1% false positives at the P < .005 level. EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) from 43 normal adult subjects were imported into the Key Institute's LORETA program. We then used the Key Institute's cross-spectrum and the Key Institute's LORETA output files (*.lor) as the 2,394 gray matter pixel representation of 3-dimensional currents at different frequencies. The mean and standard deviation *.lor files were computed for each of the 2,394 gray matter pixels for each of the 43 subjects. Tests of Gaussianity and different transforms were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of parametric vs. non-parametric statistics were compared using a "leave-one-out" cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects. Log10 transforms approximated Gaussian distribution in the range of 95% to 99% accuracy. Parametric Z score tests at P < .05 cross-validation demonstrated an average misclassification rate of approximately 4.25%, and range over the 2,394 gray matter pixels was 27.66% to 0.11%. At P < .01 parametric Z score cross-validation false positives were 0.26% and ranged from 6.65% to 0% false positives. The non-parametric Key Institute's t-max statistic at P < .05 had an average misclassification error rate of 7.64% and ranged from 43.37% to 0.04% false positives. The nonparametric t-max at P < .01 had an average misclassification rate of 6.67% and ranged from 41.34% to 0% false positives of the 2,394 gray matter pixels for any cross-validated normal subject. In conclusion, adequate approximation to Gaussian distribution and high cross-validation can be achieved by the Key Institute's LORETA programs by using a log10 transform and parametric statistics, and parametric normative comparisons had lower false positive rates than the non-parametric tests.

Rethinking Racial Profiling: A Critique of the Economics, Civil Liberties, and Constitutional Literature, and of Criminal Profiling More Generally

Rethinking Racial Profiling: A Critique of the Economics, Civil Liberties, and Constitutional Literature, and of Criminal Profiling More Generally

A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree

We present a procedure for the identification of clusters in multivariate data sets, based on the comparison between the k nearest neighbors graph, G k , and the minimal spanning tree, MST. Our key statistic is the random quantity k ̃ ≔ the smallest k such that G k contains the MST. Under regularity assumptions, we show that for i.i.d. data from a density on R d with compact support having one connected component, k ̃ = O Pr ( log n) , where n denotes sample size, a bound that seems to be sharp, according to simulations. This leads to a consistent test for the identification of crisp clusters. We illustrate the use of our procedure with an example.

The Minimal Local-Asperity Hypothesis of Early Retinal Lateral Inhibition

Recently we found that the theories related to information theory existent in the literature cannot explain the behavior of the extent of the lateral inhibition mediated by retinal horizontal cells as a function of background light intensity. These theories can explain the fall of the extent from intermediate to high intensities, but not its rise from dim to intermediate intensities. We propose an alternate hypothesis that accounts for the extent's bell-shape behavior. This hypothesis proposes that the lateral-inhibition adaptation in the early retina is part of a system to extract several image attributes, such as occlusion borders and contrast. To do so, this system would use prior probabilistic knowledge about the biological processing and relevant statistics in natural images. A key novel statistic used here is the probability of the presence of an occlusion border as a function of local contrast. Using this probabilistic knowledge, the retina would optimize the spatial profile of lateral inhibition to minimize attribute-extraction error. The two significant errors that this minimization process must reduce are due to the quantal noise in photoreceptors and the straddling of occlusion borders by lateral inhibition.

Window Subsampling of Estimating Functions with Application to Regression Models

We propose a subsampling method for estimating the asymptotic standard error of a statistic β n that is the solution to an estimating equation 1/n Σn j=1 Uj(Y j Xj, β) = 0 where the data Yj may be temporally or spatially correlated and the estimating function may depend on covariates Xj . A key statistic that we consider in detail is a generalized linear model regression coefficient computed under the assumption of independence. The availability of a consistent variance estimator allows semiparametric regression approaches for clustered and longitudinal data to be used with time series and spatial data. The methods that we develop extend the subsampling ideas of Carlstein, Sherman, and Garcia-Soidan and Hall to estimating functions. Our approach provides an attractive alternative to the jackknife method of Lele, particularly for large datasets, because we do not require parameter reestimation.

Forecasting for the generation of trading signals in financial markets

In this paper we show that optimal trading results can be achieved if we can forecast a key summary statistic of future prices. Consider the following optimization problem. Let the return ri (over time i=1, 2, ..., n) for the ith day be given and the investor has to make investment decision di on the ith day with di=1 representing a ‘long' position and di=0 a ‘neutral' position. The investment return is given by r=Σni=1ridi−cΣn+1i=1∣di−di−1∣, where c is the transaction cost. The mathematical programming problem of choosing d1, ..., dn to maximize r under a given transaction cost c is shown to have an analytic solution, which is a function of a key summary statistic called the largest change before reversal. The largest change before reversal is recommended to be used as an output in a neural network for the generation of trading signals. When neural network forecasting is applied to a dataset of Hang Seng Index Futures Contract traded in Hong Kong, it is shown that forecasting the largest change before reversal outperforms the k-step-ahead forecast in achieving higher trading profits. Copyright © 2000 John Wiley & Sons, Ltd.

Forecasting foreground impact on cosmic microwave background measurements

We explore the possible impact of galactic and extragalactic foregrounds on measurements of the cosmic microwave background (CMB). We find that, given our present understanding of the foregrounds, they are unlikely qualitatively to affect the ability of the MAP and Planck satellites to determine the angular power spectrum of the CMB, the key statistic for constraining cosmological parameters. Sufficiently far from the galactic plane, the only foregrounds that will affect power spectrum determination with any significance are the extragalactic ones. For MAP we find that the most troublesome foregrounds are radio point sources and the thermal Sunyaev-Zeldovich (SZ) effect. For Planck they are these same radio point sources and the far infrared background. Prior knowledge of the statistics of the SZ component (via either theoretical calculation, or higher frequency observations of just a few per cent of the sky, such as will be achieved by balloon borne experiments) may significantly improve MAP's determination of the CMB power spectrum. We also explore the foreground impact on MAP and Planck polarization power spectrum measurements.