Prevalence of obesity and overweight and associated nutritional factors in a population-based Swiss sample: an opportunity to analyze the impact of three different European cultural roots.

PURPOSE: Obesity represents a growing public health concern worldwide. The latest data in Switzerland rely on self-reported body mass index (BMI), leading to underestimation of prevalence. We reassessed the prevalence of obesity and overweight in a sample of the Swiss population using measured BMI and waist circumference (WC) and explored the association with nutritional factors and living in different linguistic-cultural regions. METHODS: Data of 1,505 participants of a cross-sectional population-based survey in the three linguistic regions of Switzerland were analyzed. BMI and WC were measured, and a 24-h urine collection was performed to evaluate dietary sodium, potassium and protein intake. RESULTS: The prevalence of overweight, obesity and abdominal obesity was 32.2, 14.2 and 33.6 %, respectively. Significant differences were observed in the regional distribution, with a lower prevalence in the Italian-speaking population. Low educational level, current smoking, scarce physical activity and being migrant were associated with an higher prevalence of obesity. Sodium, potassium and protein intake increased significantly across BMI categories. CONCLUSIONS: Obesity and overweight affect almost half of the Swiss adolescents and adults, and the prevalence appears to increase. Using BMI and WC to define obesity led to different prevalences. Differences were furthermore observed across Swiss linguistic-cultural regions, despite a common socio-economic and governmental framework. We found a positive association between obesity and salt intake, with a potential deleterious synergistic effect on cardiovascular risk.

Allelic proportions of 16 STR loci-including the new European Standard Set (ESS) loci-in a Swiss population sample.

Allele frequencies and forensically relevant population statistics of 16 STR loci, including the new European Standard Set (ESS) loci, were estimated from 668 unrelated individuals of Caucasian appearance living in different parts of Switzerland. The samples were amplified with a combination of the following three kits: AmpFlSTR® NGM SElect?, PowerPlex® ESI17 and PowerPlex® ESX 17. All loci were highly polymorphic and no significant departure from Hardy-Weinberg equilibrium and linkage equilibrium was detected after correction for sampling.

Sample Policy Prohibiting Harassment in the Workplace, 2006

A key component in preventing harassment is having each employer develop and implement a policy which prohibits harassment in the workplace. Having such a policy in place is also an important part of an employer’s defense should a harassment complaint be filed against the employer. This policy should be separate from and in addition to a general anti-discrimination policy. A good policy will set forth procedures that will encourage victims to come forward, that will protect confidentiality of the persons involved, that guards against retaliation, and that brings complaints to a resolution.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Finite sample nonparametric tests for linear regressions

We introduce several exact nonparametric tests for finite sample multivariatelinear regressions, and compare their powers. This fills an important gap inthe literature where the only known nonparametric tests are either asymptotic,or assume one covariate only.

Multi-sample analysis of moment-structures: Asymptotic validity of inferences based on second-order moments

In moment structure analysis with nonnormal data, asymptotic valid inferences require the computation of a consistent (under general distributional assumptions) estimate of the matrix $\Gamma$ of asymptotic variances of sample second--order moments. Such a consistent estimate involves the fourth--order sample moments of the data. In practice, the use of fourth--order moments leads to computational burden and lack of robustness against small samples. In this paper we show that, under certain assumptions, correct asymptotic inferences can be attained when $\Gamma$ is replaced by a matrix $\Omega$ that involves only the second--order moments of the data. The present paper extends to the context of multi--sample analysis of second--order moment structures, results derived in the context of (simple--sample) covariance structure analysis (Satorra and Bentler, 1990). The results apply to a variety of estimation methods and general type of statistics. An example involving a test of equality of means under covariance restrictions illustrates theoretical aspects of the paper.

Scaled and adjusted restricted tests in multi-sample analysis of moment structures

We extend to score, Wald and difference test statistics the scaled and adjusted corrections to goodness-of-fit test statistics developed in Satorra and Bentler (1988a,b). The theory is framed in the general context of multisample analysis of moment structures, under general conditions on the distribution of observable variables. Computational issues, as well as the relation of the scaled and corrected statistics to the asymptotic robust ones, is discussed. A Monte Carlo study illustrates thecomparative performance in finite samples of corrected score test statistics.

Bringing game theory to hypothesis testing: Establishing finite sample bounds on inference

Small sample properties are of fundamental interest when only limited data is avail-able. Exact inference is limited by constraints imposed by speci.c nonrandomizedtests and of course also by lack of more data. These e¤ects can be separated as we propose to evaluate a test by comparing its type II error to the minimal type II error among all tests for the given sample. Game theory is used to establish this minimal type II error, the associated randomized test is characterized as part of a Nash equilibrium of a .ctitious game against nature.We use this method to investigate sequential tests for the di¤erence between twomeans when outcomes are constrained to belong to a given bounded set. Tests ofinequality and of noninferiority are included. We .nd that inference in terms oftype II error based on a balanced sample cannot be improved by sequential sampling or even by observing counter factual evidence providing there is a reasonable gap between the hypotheses.

A dilution cost approach to financial intermediation and securities markets

This paper proposes a model of financial markets and corporate finance,with asymmetric information and no taxes, where equity issues, Bankdebt and Bond financing may all co-exist in equilibrium. The paperemphasizes the relationship Banking aspect of financial intermediation:firms turn to banks as a source of investment mainly because banks aregood at helping them through times of financial distress. The debtrestructuring service that banks may offer, however, is costly. Therefore,the firms which do not expect to be financially distressed prefer toobtain a cheaper market source of funding through bond or equity issues.This explains why bank lending and bond financing may co-exist inequilibrium. The reason why firms or banks also issue equity in our modelis simply to avoid bankruptcy. Banks have the additional motive that theyneed to satisfy minimum capital adequacy requeriments. Several types ofequilibria are possible, one of which has all the main characteristics ofa "credit crunch". This multiplicity implies that the channels of monetarypolicy may depend on the type of equilibrium that prevails, leadingsometimes to support a "credit view" and other times the classical "moneyview".

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.

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.

Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the datageneration process and in the functional form that establishes therelationship between the parameters and the data) regarding the poorperformance of the Generalized Method of Moments (GMM) in small samples.To this purpose I build a sequence of models starting with a simple linearmodel and enlarging it progressively until I approximate a standard (nonlinear)neoclassical growth model. I then use simulation techniques to find the smallsample distribution of the GMM estimators in each of the models.