Displaying similar documents to “Unravelling ecological analysis.”

Study of Bootstrap Estimates in Cox Regression Model with Delayed Entry

Silvie Bělašková, Eva Fišerová, Sylvia Krupičková (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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In most clinical studies, patients are observed for extended time periods to evaluate influences in treatment such as drug treatment, approaches to surgery, etc. The primary event in these studies is death, relapse, adverse drug reaction, or development of a new disease. The follow-up time may range from few weeks to many years. Although these studies are long term, the number of observed events is small. Longitudinal studies have increased the importance of statistical methods for time-to...

Distance-based regression in prediction of solar flare activity.

Anna Bartkowiak, Maria Jakimiec (1994)

Qüestiió

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Short-term prediction of solar flare activity using multiple regression methods was considered. The variables describing active regions the given day were used to predict the flare activity on the next day. Two groups of observational data covering the years 1988 and 1989 were dealt with. Some variants of the distance-based regression as proposed by Cuadras and Arenas (1990) appeared to be superior to the ordinary least squares method by describing more accurately the data sets under...

Product expenditure patterns in the ECPF survey: an analysis using multiple group latent-variables models.

Eva Ventura, Albert Satorra (2001)

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Using data form the Spanish household budget survey, we investigate some aspects of household heterogeneity on several product expenditures. We adopt a latent-variable model approach to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the...

Quantile plots in the analysis of heteroscedastic models.

Montserrat Pepió Viñals, Carlos Polo Miranda (1992)

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Recent developments in quality engineering methods have led to considerable interest in the analysis of variance, buiding a dispersion model, identifying important effects from replicated experiments and checking for significance by means of a half-normal plot. A methodology based on a chi-squared quantile plot is presented here for checking first the presence of heteroscedasticity, outliers and other data peculiarities, and after the estimation stage a new stepwise procedure tests for...

Small-area estimation using adjustment by covariantes.

Nicholas T. Longford (1996)

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Linear regression models with random effects are applied to estimating the population means of indirectly measured variables in small areas. The proposed method, a hybrid with design- and model-based elements, takes account of the area-level variation and of the uncertainty about the fitted regression model and the area-level population means of the covariates. The method is illustrated on data from the U.S. Department of Labor Literacy Surveys and is informally validated on two states,...

Application of HLM to data with multilevel structure

Vítor Valente, Teresa A. Oliveira (2011)

Discussiones Mathematicae Probability and Statistics

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Many data sets analyzed in human and social sciences have a multilevel or hierarchical structure. By hierarchy we mean that units of a certain level (also referred micro units) are grouped into, or nested within, higher level (or macro) units. In these cases, the units within a cluster tend to be more different than units from other clusters, i.e., they are correlated. Thus, unlike in the classical setting where there exists a single source of variation between observational units,...