The Synthetic Control Chart Based on Two Sample Variances for Monitoring the Covariance Matrix

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.

Two-dimensional tests of wave transmission and reflection characteristics of laboratory breakwaters /

"June 1980."

Food-chain relationships of iodine-131 following two nuclear tests in Nevada,

Mode of access: Internet.

Recirculation principle for ground effect machine two-dimensional tests.

"Task 9R99-01-005-04. Contract DA 44-177-TC-710."

Reporting program evaluations : two sample community mental health center annual evaluation reports /

Mode of access: Internet.

Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics

2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.

Testing for Proportional Hazards with Unrestricted Univariate Unobserved Heterogeneity

We develop tests of the proportional hazards assumption, with respect to a continuous covariate, in the presence of unobserved heterogeneity with unknown distribution at the individual observation level. The proposed tests are specially powerful against ordered alternatives useful for modeling non-proportional hazards situations. By contrast to the case when the heterogeneity distribution is known up to …nite dimensional parameters, the null hypothesis for the current problem is similar to a test for absence of covariate dependence. However, the two testing problems di¤er in the nature of relevant alternative hypotheses. We develop tests for both the problems against ordered alternatives. Small sample performance and an application to real data highlight the usefulness of the framework and methodology.

Association between breast-feeding practices and sucking habits: A cross-sectional study of children in their first year of life

In addition to providing nutrition and immunological protection, breast-feeding has positive effects on the development of the infant's oral cavity. The aim of the present study is to assess breast-feeding patterns and to analyze the influence of breast-feeding practices and maternal sociodemographic variables on the prevalence of non-nutritive sucking habits in a sample of Brazilian infants. This cross-sectional study was carried out in Southern Brazil. A random sample of 100 mothers of infants up to 12 months of age was interviewed during the National Vaccination Campaign Day. The prevalence and median duration of breast-feeding were assessed. Breast-feeding practice, the exposure factor, was categorized as exclusive breast-feeding, predominant breast-feeding, complementary breast-feeding, or weaning. Maternal sociodemographic variables included age, race, marital status, educational level, profession, and family income. The outcome investigated was the prevalence of sucking habits (pacifier use and thumb sucking). We used two-sample tests, the chi-square test and Fisher exact test0 for statistical analyses of the data. The study revealed that 75% of infants were being breast-fed. Pacifier use and thumb sucking were common in 55%. Bottle-feeding was prevalent in 74% of infants. Breast-feeding was negatively correlated with pacifier use and thumb sucking (OR = 0.11; 95% CI: 0.03 to 0.4). Bottle-feeding was strongly associated with weaning (p = 0.0003). Among the sociodemographic variables, only marital status showed a statistical association with sucking habits (p = 0.04). These findings suggest that breast-feeding can prevent the occurrence of sucking habits. Although we could not evaluate causality assessment, malocclusion prevention seems to be yet one more reason for promoting breast-feeding practices.

Statistical tests for scaling in the inter-event times of earthquakes in California

We explore in depth the validity of a recently proposed scaling law for earthquake inter-event time distributions in the case of the Southern California, using the waveform cross-correlation catalog of Shearer et al. Two statistical tests are used: on the one hand, the standard two-sample Kolmogorov-Smirnov test is in agreement with the scaling of the distributions. On the other hand, the one-sample Kolmogorov-Smirnov statistic complemented with Monte Carlo simulation of the inter-event times, as done by Clauset et al., supports the validity of the gamma distribution as a simple model of the scaling function appearing on the scaling law, for rescaled inter-event times above 0.01, except for the largest data set (magnitude greater than 2). A discussion of these results is provided.