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Secular trends in body weight and length of children aged 0–2 years. Longitudinal study of five consecutive birth cohorts between 1964–2003 from Wrocław, Poland

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Borysławski K., Kozieł S.
Anthropological Review
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2016
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vol. 79
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issue 3
301-310
EN
The aim of this work is to evaluate the intensity of possible secular trends among the five subsequent cohorts of Wrocław (Poland) children aged 0, 6, 12 and 24 months. This document describes secular changes in the body length, weight and the Rohrer’s index. Material: Research material represent the longitudinal studies of five consecutive birth cohorts. The first study involved children born during 1963-1965, and the last in 2003-2005. All of the studies were related to the same social group and were conducted using the same methodology. There are differences in the intensity and direction of the secular trends in children depending on their age. In both sexes the body length of newborns kept increasing until the end of the nineties and decreased in the last decade. The body weight did not change during the 40-year period. This suggests an important role of maternal regulator in fetal development and therefore no clear response to external environmental factors. Secular changes such as the body length and weight, which are the most adequate to the economic changes in Poland, were observed in children aged 6 and 12 months. It may be a result of their highest ecosensitivity during this period. However, there have not been any clear trends observed in the 24 months age group. This may be due to the increasing participation of genetic factors in the development of the child.
2
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Construction and Use of a Relational Database to Analyze the Careers of Professional Politicians in Poland, 1985–2007

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Nyćkowiak J.
ASK. Research and Methods
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2014
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vol. 23
115-134
EN
This article describes a unique version of a longitudinal database featuring the Eastern Europe Parliamentarian and Candidate Database (EAST PaC). This version contains contextual and biographical information on career politicians from Poland, 1985-2007. I define career politicians as those who, three times during the period covered in the data, were granted a parliamentary seat in either the upper (Senat) or lower house (Sejm). As part of this project, I reconstructed the political biographies of career politicians. Based on practical experience in using these data, I propose the use of relational databases as a best practices approach to manage data of this type. I describe the need for relational databases in general, and describe in detail how to apply them to longitudinal data consisting of parliamentarians and their contexts.
3
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Functional principal components analysis on the example of the achievements of students in the years 2009-2017

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Sztemberg-Lewandowska M.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2019
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issue vol. 23 no. 4
16-29
EN
The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA on the functional data including curves and trajectories, i.e. a series of individual observations, not a single observation, as usual. The functional principal components analysis with functional data, will be used in the analysis. This method allows the analysis of dynamic data. The purpose of the article is to apply of functional principal components analysis to the problem of student’s achievements. The article was compared the level of students' knowledge during different stages of education in 2009-2017. The analysis covers the average exam results after the II, III and IV stage of education.
4
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Graphical tools of discrete longitudinal data presentation in R

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Genge E.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2019
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issue vol. 23 no. 3
26-39
EN
Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.
5
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On Pseudo-EBLUP Under Some Model for Longitudinal Data with Auxiliary Variables

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Żądło T., Uniwersytet Ekonomiczny w Katowicach; Wydział Zarządzania; Katedra Statystyki
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
The problem of modeling longitudinal profiles is considered assuming that the population and elements affiliation to subpopulations may change in time. The considerations are based on a model with auxiliary variables for longitudinal data with element and subpopulation specific random components (compare Verbeke, Molenberghs, 2000; Hedeker, Gibbons, 2006) which is a special case of the General Linear Model (GLM) the General Linear Mixed Model (GLMM). In the paper the pseudo-empirical best linear unbiased predictor (Pseudo-EBLUP) based on model-assisted approach will be presented along with its mean squared error (MSE) and its estimators. In the simulation study its accuracy will be compared with some calibration estimators which are based on model-assisted approach too.
6
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On some calibration estimators of subpopulation total for longitudinal data

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Żądło T.
Acta Universitatis Lodziensis. Folia Oeconomica
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2011
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vol. 252
EN
The problem of modeling longitudinal profiles is considered assuming that the population and elements affiliation to subpopulation may change in time. The considerations are based on a model with auxiliary variables for longitudinal data with subject specific (in this case - element and subpopulation specific) random components (compare Verbeke, Molenberghs, 2000; Hedeker, Gibbons, 2006) which is a special case of the General Linear Mixed Model. In the paper calibration estimators of subpopulation total for data from one period are presented and some modifications for the case of longitudinal data are proposed. Design-based mean squared errors and its estimators are also presented. In the simulation study accuracy of the estimators is compared with Horvitz-Thomson estimator and the best empirical linear unbiased predictor derived for the considered model.
7
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On Parameter Estimation of Some Longitudinal Model

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Żądło T., Katowice U. o. E.
Acta Universitatis Lodziensis. Folia Oeconomica
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2013
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vol. 285
EN
The problem of modeling longitudinal profiles is considered assuming that the population and elements’ affiliation to subpopulations may change in time. Some longitudinal model which is a special case of the general linear model (GLM) and the general linear mixed model (GLMM) is studied. In the model two random components are included under assumptions of simultaneous spatial autoregressive process (SAR) and temporal first-order autoregressive process (AR(1)) respectively. The accuracy of model parameters’ restricted maximum likelihood estimators is considered in the simulation.
PL
Rozważany jest problem modelowania profili wielookresowych zakładając, że populacja i przynależność elementów domen mogą zmieniać się w czasie. Proponowany model jest przypadkiem szczególnym ogólnego modelu liniowego i ogólnego mieszanego modelu liniowego. W modelu tym uwzględniono dwa wektory składników losowych spełniające odpowiednio założenia przestrzennego modelu autoregresyjnego i modelu autoregresyjnego rzędu pierwszego w czasie. W symulacji rozważano dokładność estymatorów parametrów modelu uzyskanych metodą największej wiarygodności z ograniczeniami.
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Longitudinal BMI percentile curves by maturity status of Japanese children

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Mino T., Malina R. M., Nariyama K.
Anthropological Review
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2012
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vol. 75
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issue 1
33-40
EN
The objective of the research was to prepare longitudinal percentile curves for the BMI (kg/m2) relative to time before and after peak height velocity (PHV) in Japanese boys and girls born between 1989 and 1991. Stature and weight were measured in every April from 6.5 to 16.5 years for 283 boys and 480 girls. Age at PHV was estimated by the proportional allotment method. The 50th percentile curves for the BMI of Japanese boys and girls born between 1989 and 1991 were similar to the corresponding curves for Japanese boys and girls born between 1972 and 1974. However, the 97th percentiles of children born between 1989 and 1991 were higher and the corresponding 3rd percentiles were lower compared to children born between 1972 and 1974. The differences can be attributed to the influence of early maturing children born between 1989 and 1991 on the 97th percentiles and of late maturing children born between 1989 and 1991 on the 3rd percentiles. The results highlight the need to consider the timing of maturity, in this case, age at PHV, when interpreting the BMI of adolescents.
9

Funkcjonalna analiza głównych składowych w badaniu zmian liczby studentów w czasie w krajach europejskich

63%
Sej-Kolasa M., Sztemberg-Lewandowska M.
Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie
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2013
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issue 916
71-81
PL
Analiza głównych składowych (PCA) polega na transformacji zmiennych pierwotnych w zbiór nowych wzajemnie ortogonalnych zmiennych, zwanych głównymi składowymi. Funkcjonalna analiza głównych (FPCA) składowych ma zalety klasycznej analizy głównych składowych, dodatkowo umożliwia analizę danych o charakterze dynamicznym. Podstawową różnicą między tymi dwiema metodami jest rodzaj danych: PCA bazuje na danych wielowymiarowych, natomiast FPCA na danych funkcjonalnych. Danymi funkcjonalnymi są krzywe i trajektorie, czyli ciąg indywidualnych obserwacji, a nie pojedyncza obserwacja. Celem artykułu jest pokazanie możliwości wykorzystania funkcjonalnej analizy głównych składowych do badania zjawisk opisanych danymi wzdłużnymi (longitudinal data). Przykład wykorzystania tej metody omówiony w artykule opiera się na analizie zmiany liczby studentów w czasie w wybranych krajach europejskich. Możliwości wizualizacyjne metody pozwalają na porównanie krajów i wyodrębnienie obserwacji odstających.
EN
Principal component analysis (PCA) transforms an original set of variables into a new orthogonal set called principal components. Functional principal component analysis (FPCA) has the same advantages as classical principal component analysis while also enabling the analysis of dynamic data. The main difference between them is that PCA is based on multidimensional data and FPCA is based on functional data. The functional data are curves, surfaces or anything else varying over a continuum. They are not a single observation. The main aim of the paper is to show the usefulness of applying functional principal component analysis in order to analyse longitudinal data. The paper presents an example of how this method has been used based on the analysis of changes in the number of students (over time) in chosen European countries. Visualisation of the results makes it possible to compare countries and detect outliers.
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