ECLAC Development of official statistics in the region. Report of the Economic Commission for Latin America and the Caribbean on the development of official statistics in Latin America and the Caribbean

This document was prepared by the Economic Commission for Latin America and the Caribbean (ECLAC); and offers a description of the main trends in the development of official statistics in Latin America and the Caribbean and the principal challenges in that regard.The first chapter provides an analysis of the state of development of statistical production in the region, based on statistical information for 41 Latin American and Caribbean countries and eight specific areas. The institutional organization of national statistical systems in the region is also described. Chapter II examines the history and current status of mechanisms for regional and subregional coordination and of the Statistical Conference of the Americas of ECLAC. Chapter III describes the main challenges for official statistics in the countries of the region and the strategies that the Statistical Conference of the Americas and ECLAC propose to implement in order to address them.

An assessment of big data for official statistics in the Caribbean: Challenges and opportunities

The data revolution for sustainable development has triggered interest in the use of big data for official statistics such that theUnited Nations Economic and Social Council considers it to be almost an obligation for statistical organizations to explore big data. Big data has been promoted as a more timely and cheaper alternative to traditional sources of official data, and one that offers great potential for monitoring the sustainable development goals. However, privacy concerns, technology and capacity remain significant obstacles to the use of big data. This study makes a case for incorporating big data in official statitics in the Caribbean by highlight the opportunities that big data provides for the subregion, while suggesting ways to manage the challenges. It serves as a starting point for further discussions on the many facets of big data and provides an initial platform upon which a Caribbean big data strategy could be built.

"Let's go!" 10 years' retrospect of the World War; historical official statistics, copies of documents and intimate anecdotes pertaining to the 1 Division, United States Army, from June 14th, 1917, to November 11th, 1918,

Lettered on cover of this copy: Nelson M. Holderman, M.H., Captain U.S. Army, retired.

The continuing evolution of official statistics: some challenges and opportunities

As economies, societies, and environments change, official statistics evolve and develop to reflect those changes. In reaction to disruptive innovations arising from globalisation, technological advances, and cultural changes, the pace of change of official statistics will accelerate in the future. The motivation for change may also be more existential than that of the past as official statisticians consider the survival of their discipline. This article examines some of the emerging developments and questions whether they present threats or offer opportunities.

Improving small area estimation by combining surveys: new perspectives in regional statistics

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.

Proposal for a code of good practice in statistics in Latin America and the Caribbean and implementation plan for 2012-2013

Incluye Bibliografía

Summary of commonwealth production statistics for the years

At head of title: Official statistics, commonwealth of Australia. Production bulletin

Fiskeristatistikk = Fishery statistics.

Vols. published 1994- have series title also in English: Official statistics of Norway.

High prevalence of drug-resistant tuberculosis and other mycobacteria among HIV-infected patients in Brazil: a systematic review

There is a little-noticed trend involving human immunodeficiency virus (HIV)-infected patients suspected of having tuberculosis: the triple-treatment regimen recommended in Brazil for years has been potentially ineffective in over 30% of the cases. This proportion may be attributable to drug resistance (to at least 1 drug) and/or to infection with non-tuberculous mycobacteria. This evidence was not disclosed in official statistics, but arose from a systematic review of a few regional studies in which the diagnosis was reliably confirmed by mycobacterial culture. This paper clarifies that there has long been ample evidence for the potential benefits of a four-drug regimen for co-infected patients in Brazil and it reinforces the need for determining the species and drug susceptibility in all positive cultures from HIV-positive patients.

Chronic obstructive pulmonary disease in Brazil: mortality and hospitalization trends and rates, 1996-2008

SETTING: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death among adults in Brazil. OBJECTIVE: To evaluate the mortality and hospitalisation trends in Brazil caused by COPD during the period 1996-2008. DESIGN: We used the health official statistics system to obtain data about mortality (1996-2008) and morbidity (1998-2008) due to COPD and all respiratory diseases (tuberculosis: codes A15-16; lung cancer: code C34, and all diseases coded from J40 to 47 in the 10th Revision of the International Classification of Diseases) as the underlying cause, in persons aged 45-74 years. We used the Joinpoint Regression Program log-linear model using Poisson regression that creates a Monte Carlo permutation test to identify points where trend lines change significantly in magnitude/direction to verify peaks and trends. RESULTS: The annual per cent change in age-adjusted death rates due to COPD declined by 2.7% in men (95%CI -3.6 to -1.8) and -2.0% (95%CI -2.9 to -1.0) in women; and due to all respiratory causes it declined by -1.7% (95%CI 2.4 to -1.0) in men and -1.1% (95%CI -1.8 to -0.3) in women. Although hospitalisation rates for COPD are declining, the hospital admission fatality rate increased in both sexes. CONCLUSION: COPD is still a leading cause of mortality in Brazil despite the observed decline in the mortality/hospitalisation rates for both sexes.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.