La captación estadística de los asalariados agropecuarios : Reflexiones en torno a sus problemas mediante un ejercicio de comparación entre censos de población y agropecuarios (Argentina, 2001/2002)

Históricamente la captación estadística del empleo agropecuario (y más específicamente del empleo asalariado agropecuario) ha presentado una serie de problemas y limitaciones para las fuentes de datos tradicionales. Los elevados niveles de transitoriedad, estacionalidad, no registro e informalidad han tenido como consecuencia una serie de dificultades en su cuantificación por medio de las fuentes censales y muestrales tradicionales. Los procesos más recientes que atraviesan a esta fracción social (urbanización, acortamiento de ciclos productivos y ocupacionales, disminución de requerimientos de fuerza de trabajo por la mecanización de ciertas cosechas, etc.) parecen haber incrementado dichas dificultades. Trabajos previos realizados demuestran que los censos de población y agropecuarios arrojan diferentes resultados en la cuantificación de los asalariados del sector. Se presenta en este documento un análisis comparado de los resultados obtenidos en Argentina, por el Censo Nacional de Población y Vivienda de 2001 y el Censo Nacional Agropecuario de 2002. El objetivo buscado es realizar una aproximación a las diferentes cifras de asalariados en el agro que arrojan ambos relevamientos en todos los departamentos del país. A su vez, se intentará vincular dichas diferencias con los distintos territorios y distintas estructuras sociales y agrarias, buscando descubrir si permiten aportar a la explicación de aquellos resultados divergentes. Para ello se confeccionó una base de datos del total del país, desagregada a nivel provincial y departamental (máximo nivel de desagregación permitida por las fuentes publicadas) de la cantidad total de asalariados agropecuarios y diversos indicadores de la estructura social y agraria (cantidad de explotaciones pobres, niveles de urbanización, distribución de la tierra, etc.).

La captación estadística de los asalariados agropecuarios : Reflexiones en torno a sus problemas mediante un ejercicio de comparación entre censos de población y agropecuarios (Argentina, 2001/2002)

Históricamente la captación estadística del empleo agropecuario (y más específicamente del empleo asalariado agropecuario) ha presentado una serie de problemas y limitaciones para las fuentes de datos tradicionales. Los elevados niveles de transitoriedad, estacionalidad, no registro e informalidad han tenido como consecuencia una serie de dificultades en su cuantificación por medio de las fuentes censales y muestrales tradicionales. Los procesos más recientes que atraviesan a esta fracción social (urbanización, acortamiento de ciclos productivos y ocupacionales, disminución de requerimientos de fuerza de trabajo por la mecanización de ciertas cosechas, etc.) parecen haber incrementado dichas dificultades. Trabajos previos realizados demuestran que los censos de población y agropecuarios arrojan diferentes resultados en la cuantificación de los asalariados del sector. Se presenta en este documento un análisis comparado de los resultados obtenidos en Argentina, por el Censo Nacional de Población y Vivienda de 2001 y el Censo Nacional Agropecuario de 2002. El objetivo buscado es realizar una aproximación a las diferentes cifras de asalariados en el agro que arrojan ambos relevamientos en todos los departamentos del país. A su vez, se intentará vincular dichas diferencias con los distintos territorios y distintas estructuras sociales y agrarias, buscando descubrir si permiten aportar a la explicación de aquellos resultados divergentes. Para ello se confeccionó una base de datos del total del país, desagregada a nivel provincial y departamental (máximo nivel de desagregación permitida por las fuentes publicadas) de la cantidad total de asalariados agropecuarios y diversos indicadores de la estructura social y agraria (cantidad de explotaciones pobres, niveles de urbanización, distribución de la tierra, etc.).

La captación estadística de los asalariados agropecuarios : Reflexiones en torno a sus problemas mediante un ejercicio de comparación entre censos de población y agropecuarios (Argentina, 2001/2002)

Históricamente la captación estadística del empleo agropecuario (y más específicamente del empleo asalariado agropecuario) ha presentado una serie de problemas y limitaciones para las fuentes de datos tradicionales. Los elevados niveles de transitoriedad, estacionalidad, no registro e informalidad han tenido como consecuencia una serie de dificultades en su cuantificación por medio de las fuentes censales y muestrales tradicionales. Los procesos más recientes que atraviesan a esta fracción social (urbanización, acortamiento de ciclos productivos y ocupacionales, disminución de requerimientos de fuerza de trabajo por la mecanización de ciertas cosechas, etc.) parecen haber incrementado dichas dificultades. Trabajos previos realizados demuestran que los censos de población y agropecuarios arrojan diferentes resultados en la cuantificación de los asalariados del sector. Se presenta en este documento un análisis comparado de los resultados obtenidos en Argentina, por el Censo Nacional de Población y Vivienda de 2001 y el Censo Nacional Agropecuario de 2002. El objetivo buscado es realizar una aproximación a las diferentes cifras de asalariados en el agro que arrojan ambos relevamientos en todos los departamentos del país. A su vez, se intentará vincular dichas diferencias con los distintos territorios y distintas estructuras sociales y agrarias, buscando descubrir si permiten aportar a la explicación de aquellos resultados divergentes. Para ello se confeccionó una base de datos del total del país, desagregada a nivel provincial y departamental (máximo nivel de desagregación permitida por las fuentes publicadas) de la cantidad total de asalariados agropecuarios y diversos indicadores de la estructura social y agraria (cantidad de explotaciones pobres, niveles de urbanización, distribución de la tierra, etc.).

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system.

Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

CD4(+) T cell count decreases by ethnicity among untreated patients with HIV infection in South Africa and Switzerland.

BACKGROUND: Estimates of the decrease in CD4(+) cell counts in untreated patients with human immunodeficiency virus (HIV) infection are important for patient care and public health. We analyzed CD4(+) cell count decreases in the Cape Town AIDS Cohort and the Swiss HIV Cohort Study. METHODS: We used mixed-effects models and joint models that allowed for the correlation between CD4(+) cell count decreases and survival and stratified analyses by the initial cell count (50-199, 200-349, 350-499, and 500-750 cells/microL). Results are presented as the mean decrease in CD4(+) cell count with 95% confidence intervals (CIs) during the first year after the initial CD4(+) cell count. RESULTS: A total of 784 South African (629 nonwhite) and 2030 Swiss (218 nonwhite) patients with HIV infection contributed 13,388 CD4(+) cell counts. Decreases in CD4(+) cell count were steeper in white patients, patients with higher initial CD4(+) cell counts, and older patients. Decreases ranged from a mean of 38 cells/microL (95% CI, 24-54 cells/microL) in nonwhite patients from the Swiss HIV Cohort Study 15-39 years of age with an initial CD4(+) cell count of 200-349 cells/microL to a mean of 210 cells/microL (95% CI, 143-268 cells/microL) in white patients in the Cape Town AIDS Cohort > or =40 years of age with an initial CD4(+) cell count of 500-750 cells/microL. CONCLUSIONS: Among both patients from Switzerland and patients from South Africa, CD4(+) cell count decreases were greater in white patients with HIV infection than they were in nonwhite patients with HIV infection.

Handling OOV Words in Phrase-Based Statistical Machine Translation for Malayalam

Statistical Machine Translation (SMT) is one of the potential applications in the field of Natural Language Processing. The translation process in SMT is carried out by acquiring translation rules automatically from the parallel corpora. However, for many language pairs (e.g. Malayalam- English), they are available only in very limited quantities. Therefore, for these language pairs a huge portion of phrases encountered at run-time will be unknown. This paper focuses on methods for handling such out-of-vocabulary (OOV) words in Malayalam that cannot be translated to English using conventional phrase-based statistical machine translation systems. The OOV words in the source sentence are pre-processed to obtain the root word and its suffix. Different inflected forms of the OOV root are generated and a match is looked up for the word variants in the phrase translation table of the translation model. A Vocabulary filter is used to choose the best among the translations of these word variants by finding the unigram count. A match for the OOV suffix is also looked up in the phrase entries and the target translations are filtered out. Structuring of the filtered phrases is done and SMT translation model is extended by adding OOV with its new phrase translations. By the results of the manual evaluation done it is observed that amount of OOV words in the input has been reduced considerably

Mixture models for capture-recapture count data

The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.

CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

Changes in Complete Blood Count in Patients With Surgically Treated Facial Fractures

The purpose of this prospective study was to verify the changes in the preoperative and postoperative complete blood counts of patients with surgically treated facial fractures. Fifty consecutive patients with a mean age of 34 years who presented facial fractures and underwent surgical treatment were included. A complete blood count was performed, comprising the red and white blood cell count (cells/mu L), hemoglobin (g/dL), and hematocrit (%) levels. These data were obtained preoperatively and postoperatively during a 6-week period. Statistical analyses were performed using the Kruskal-Wallis and Mann-Whitney tests to identify the possible differences among the groups and among the periods of observation using the Friedman and Wilcoxon matched-pairs signed-ranks tests. The most common location of the fractures was the mandible (42.3%), followed by the zygomatic-orbital (36.5%) and associated locations (21.2%). Leukocytosis was associated with neutrophilia in the immediate postoperative period in all of the groups. There were no values below the reference limits of the values of hemoglobin, hematocrit, and erythrocytes, and no values above the reference limits for the remaining white blood cells, although significant differences among periods were observed in most cells, depending on the type of fracture. The primary findings were leukocytosis associated with neutrophilia, verified in the immediate postoperative period in all of the groups, and the influence of the type of fracture on the significant alterations observed among studied periods on the values of hemoglobin, hematocrit, erythrocytes, leukocytes, neutrophils, and lymphocytes.

Polymorphonuclear leukocytes CH138+apoptosis evaluation in milk with high and low somatic cell count - preliminary data

Polymorphonuclear leukocyte (PMNL) apoptosis is central to the successful resolution of inflammation. Since Somatic Cell Count (SCC) is an indicator of the mammary gland's immune status, this study sought to clarify the influence that these factors have on each other and on the evolution of the inflammatory process. Milk samples were stained with annexin-V, propidium iodide (PI), primary antibody anti-CH138A. Negative correlation between SCC and PMNL apoptosis was found, and a statistical difference between high SCC group and low SCC group was observed concerning the rate of viable PMNL, apoptotic PMNL, necrotic PMNL and necrotic and/or apoptotic PMNL. Overall, the high cellularity group presented lower proportions of CH138+ cells undergoing apoptosis and higher proportions of viable and necrotic CH138+ cells. Thus, it can be concluded that PMNL apoptosis and SCC are related factors, and that in high SCC, milk apoptosis is delayed. Although there is a greater amount of active phagocytes in this situation, apoptosis' anti-inflammatory effects are decreased, while necrosis' pro-inflammatory effects are increased, which can contribute to chronic inflammation.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.

Minimum count sums for charcoalconcentration estimates in pollen slides: accuracy and potential errors

Charcoal particles in pollen slides are often abundant, and thus analysts are faced with the problem of setting the minimum counting sum as small as possible in order to save time. We analysed the reliability of charcoal-concentration estimates based on different counting sums, using simulated low-to high-count samples. Bootstrap simulations indicate that the variability of inferred charcoal concentrations increases progressively with decreasing sums. Below 200 items (i.e., the sum of charcoal particles and exotic marker grains), reconstructed fire incidence is either too high or too low. Statistical comparisons show that the means of bootstrap simulations stabilize after 200 counts. Moreover, a count of 200-300 items is sufficient to produce a charcoal-concentration estimate with less than+5% error if compared with high-count samples of 1000 items for charcoal/marker grain ratios 0.1-0.91. If, however, this ratio is extremely high or low (> 0.91 or < 0.1) and if such samples are frequent, we suggest that marker grains are reduced or added prior to new sample processing.