Quality of life of caregivers for patients of cerebrovascular accidents: association of (socio-demographic) characteristics and burden

OBJECTIVE Investigating the association between quality of life with socio-demographic characteristics and the burden of caregivers for individuals with cerebrovascular accident sequelae. METHOD A descriptive, cross-sectional study with a sample composed of 136 caregivers. For data collection, a semi-structured questionnaire, the Barthel, Burden Interview and Short-Form-36 scales were used. Correlation analysis, t-Student test and F-test were used for the analysis in order to compare averages. RESULTS Significant averages in quality of life were demonstrated in association with female caregivers and those over 60 years in the field 'functional capacity,' and in the domains of 'mental health' and 'vitality' for those with higher income. Regarding burden association, the highlighted areas were 'functional capacity,' 'physical aspects,' 'emotional aspects' and 'pain.' CONCLUSION The creation of public policies and social support to effectively reduce the burden on caregivers is a necessity.

Geology and correlation of the Mersin mélanges, southern Turkey

Our paper aims to give a thorough description of the infra-ophiolitic melanges associated with the Mersin ophiolite. We propose new regional correlations of the Mersin melanges with other melange-like units or similar series, located both in southern Turkey and adjacent regions. The palaeotectonic implications of the correlations are also discussed. The main results may be summarized as follows: the infra-ophiolitic melange is subdivided into two units, the Upper Cretaceous Sorgun ophiolitic melange and the Ladinian-Carnian Hacialani melange. The Mersin melanges, together with the Antalya and Mamonia domains, are represented by a series of exotic units now found south of the main Taurus range, and are characteristic of the South-Taurides Exotic Units. These melanges clearly show the mixed origin of the different blocks and broken formations. Some components have a Palaeotethyan origin and are characterized by Pennsylvanian and Lower to Middle Permian pelagic and slope deposits. These Palaeotethyan remnants, found exclusively in the Hacialani melange, were reworked as major olistostromes in the Neotethys basin during the Eo-Cimmerian orogenic event. Neotethyan elements are represented by Middle Triassic seamounts and by broken formations containing typical Neotethyan conodont faunas such as Metapolygnathus mersinensis Kozur & Moix and M. primitius s. s., both present in the latest Carnian interval, as well as the occurrence of the middle Norian Epigondolella praeslovakensis Kozur, Masset & Moix. Other elements are clearly derived from the former north Anatolian passive margin and are represented by Huglu-type series including the Upper Triassic syn-rift volcanic event. These sequences attributed to the Huglu-Pindos back-arc ocean were displaced southward during the Late Cretaceous obduction event. The Tauric elements are represented by Eo-Cimmerian flysch-like and molasse sequences intercalated in Neotethyan series. Additionally, some shallow-water blocks might be derived from the Bolkardag para-autochthonous and the Taurus-Beydaglari marginal sequences.

Correlation between the infestation level of Sitophilus zeamais Motschulsky, 1855 (Coleoptera, Curculionidae) and the quality factors of stored corn, Zea mays L. (Poaceae)

This experiment was carried out in order to evaluate the effect of Sitophilus zeamais on physical, physiological and sanitary quality of stored corn. Samples of 500 g of the hybrid OC-705, in three replicates, were conditioned in glasses covered with a screened lid, and kept in chamber at 25±2ºC, 70±5% RH and 12 h of photophase, for 150 days. The infestation levels were 0, 5, 15 and 50 adults/replicate, for the storage periods of 30, 60, 90, 120 and 150 days. The moisture content, classification, weight loss, germination and internal infestation were evaluated monthly. Significant inverse correlations were verified between the number of insects and both the germination and the weight loss; also between the internal infestation and the germination and the standard type. The presence of S. zeamais showed a positive correlation with the weight loss, what means that the internal and external infestations contribute to the reduction of physiological and physical quality of corn seeds. The mean dry matter loss was 0,36%/day, corresponding to a consumption of 0,0001%/insect/month. As the result of those damages, the product suffered reduction of the commercial grade in 30 days, with significant loss in all quality factors.

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.

Compact matrix expressions for generalized Wald tests of equality of moment vectors

Asymptotic chi-squared test statistics for testing the equality ofmoment vectors are developed. The test statistics proposed aregeneralizedWald test statistics that specialize for different settings by inserting andappropriate asymptotic variance matrix of sample moments. Scaled teststatisticsare also considered for dealing with situations of non-iid sampling. Thespecializationwill be carried out for testing the equality of multinomial populations, andtheequality of variance and correlation matrices for both normal andnon-normaldata. When testing the equality of correlation matrices, a scaled versionofthe normal theory chi-squared statistic is proven to be an asymptoticallyexactchi-squared statistic in the case of elliptical data.

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.

Illusory correlation in the remuneration of chief executive officers: It pays to play golf, and well

Illusory correlation refers to the use of information in decisions that is uncorrelated with the relevantcriterion. We document illusory correlation in CEO compensation decisions by demonstrating thatinformation, that is uncorrelated with corporate performance, is related to CEO compensation. We usepublicly available data from the USA for the years 1998, 2000, 2002, and 2004 to examine the relationsbetween golf handicaps of CEOs and corporate performance, on the one hand, and CEO compensationand golf handicaps, on the other hand. Although we find no relation between handicap and corporateperformance, we do find a relation between handicap and CEO compensation. In short, golfers earnmore than non-golfers and pay increases with golfing ability. We relate these findings to the difficultiesof judging compensation for CEOs. To overcome this and possibly other illusory correlations inthese kinds of decisions, we recommend the use of explicit, mechanical decision rules.

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.