Asymptotic robustness of standard errors in multilevel structural equation models

Data in social and behavioral sciences are often hierarchically organized. Multilevel statistical methodology was developed to analyze such data. Most of the procedures for analyzing multilevel data are derived from maximum likelihood based on the normal distribution assumption. Standard errors for parameter estimates in these procedures are obtained from the corresponding information matrix. Because practical data typically contain heterogeneous marginal skewnesses and kurtoses, this paper studies how nonnormally distributed data affect the standard errors of parameter estimates in a two-level structural equation model. Specifically, we study how skewness and kurtosis in one level affect standard errors of parameter estimates within its level and outside its level. We also show that, parallel to asymptotic robustness theory in conventional factor analysis, conditions exist for asymptotic robustness of standard errors in a multilevel factor analysis model.     

Comparison of discomfort levels obtained with pure tones and multitone complexes

The relationship between threshold of discomfort (TD) estimates and the number of components in a complex signal has been investigated. The thresholds of discomfort were first obtained for 16 pure tones located at the center frequency of critical bands from 250 to 4000 Hz. Subsequently, thresholds of discomfort were obtained for 2, 4, 8, and 16 tone complexes. The pure-tone components of the complexes were systematically selected from the same 16 pure tones. For each subject, the relative intensities of the components in the four complexes were determined in such a way so as to parallel the pure tone TD contour obtained for that subject. Data were obtained from 15 normal and 15 hearing impaired adults. The individuals in the latter group all had mild to moderate sensorineural hearing loss. Summation of discomfort (S) was defined as the difference between the threshold of discomfort for a pure tone presented in isolation and within the complex. The two groups demonstrated different summation values. For both groups, however, the summation was shown to be a linear function of the logarithm of the number of components in the complex: S = a + b log (n) where n is the number of components (2, 4, 8, 16). For the normal hearing group, a and b are 2.05 and 11.51, respectively, while for the hearing impaired group, they are 3.95 and 12.88, respectively. While the future digital hearing aids can easily regulate their limiting levels so as to accurately account for this summation, present day hearing aids may underestimate this effect.(ABSTRACT TRUNCATED AT 250 WORDS)     

DYNGA: a general purpose QM-MM-MD program. I. Application to water

We present a general purpose QM-MM-MD engine (DYNGA) designed to test alternative hybrid Hamiltonians geared towards the treatment of problems of interest in structural biology including the use of experimental data constraints. In this first presentation we use DYNGA to explore the behaviour of a traditional QM-MM approach in the treatment of the water—water interaction. We find the potential energy hypersurface for the water dimer computed with the HF 4–31G*/TIP3P hybrid Hamiltonian tends to be too flat. We also explore the effect of using traditional QM-MM techniques on proton wires and conclude there is a need for improvement, possibly addressed by using polarizable force fields.     

Asymptotic robustness of the normal theory likelihood ratio statistic for two-level covariance structure models

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures have been developed to analyze such data while taking into account the resulting dependence of observations. Most of these developments require a multivariate normality distribution assumption. It is important to know whether normal theory-based inference can still be valid when applied to nonnormal hierarchical data sets. Using an analytical approach for balanced data and numerical illustrations for unbalanced data, this paper shows that the likelihood ratio statistic based on the normality assumption is asymptotically robust for many nonnormal distributions. The result extends the scope of asymptotic robustness theory that has been established in different contexts.     

PAST AND FUTURE MANAGEMENT OF A COLLAPSED FISHERY: THE BAY OF BISCAY ANCHOVY

Abstract We analyze the efficiency of the international management of the Bay of Biscay anchovy. While a sharing agreement between France and Spain has been in place since 1992, the fish stock collapsed in 2005 and the fishery closed from 2005 to spring 2010. We consider differences in production technologies between both countries and calibrate our model using data from 1987 to 2009. Our results suggest two sources of rent dissipation under the existing sharing agreement: inefficient quota allocation and production inefficiencies due to inflexible national regulations. We discuss several alternatives to improve management.     

ConFlo III - an interface for high precision delta(13)C and delta(15)N analysis with an extended dynamic range

A newly developed interface coupling a CHN combustion device (elemental analyser 'EA') to an isotope ratio mass spectrometer is described and evaluated. The purpose of the device is to extend the dynamic range of delta(13)C and delta(15)N analysis from less than 2 orders of magnitude to more than 3 orders of magnitude. Carbon isotope ratio measurements of atropine as a model compound have been performed analysing between 1 μg to 5 mg C with acceptable to excellent precision (0.6 to 0.06 per thousand, delta-notation). The correction due to the blank signal is critical for sample amounts smaller than 4 μg C. The maximum sample weight is determined by the combustion capacity of the EA. Larger sample amounts are measured using dilution of a small part of the EA effluent with helium. The dilution mechanism works virtually free of isotope fractionation. Copyright 1999 John Wiley & Sons, Ltd.     

Inferences on Correlation Coefficients in Some Classes of Nonnormal Distributions

Correlation coefficients have many applications for studying the relationship among multivariate observations. Classical inferences on correlation coefficients are mainly based on the normality assumption. This assumption is hardly realistic in the real world, which implies that the procedures on correlation coefficients used in many statistical software packages may not be relevant to most data sets in practice. However, we show that the classical procedures, possibly after simple corrections, are also valid in classes of distributions with large skewnesses and heterogeneous marginal kurtoses. A useful class of nonnormal distributions is identified for each of several types of correlation coefficients. The marginals of these distributions may include a variety of univariate distributions with different shapes. The results generalize the classical procedures to much larger classes of distributions than previously known and give a better understanding of the historical controversy regarding the behavior of the sample correlation coefficient. An implication is that one need not be worried so much by the nonnormality of data sets when using these classical procedures, providing simple corrections are evaluated and possibly undertaken.