JournalTOCs

Lietuvos Statistikos Darbai
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This is an Open Access Journal

Open Access journal
ISSN (Print) 1392-642X - ISSN (Online) 2029-7262
Published by Vilnius University

[38 journals]

Editorial Board and Contents
- Authors: Rita Činčienė
  Pages: 1 - 1
  PubDate: 2022-12-20
  DOI: 10.15388/LJS.2022.32016
  Issue No: Vol. 61 (2022)
Using PIRLS and TIMSS data for the assessment of efficiency and
effectiveness of primary education in post-socialist and old democracy EU
member states
- Authors: Dovilė Stumbrienė, Rimantas Želvys, Julius Žilinskas, Audronė Jakaitienė
  Pages: 1 - 15
  Abstract: It is a data-oriented approach to evaluate the performance of a set of decision-making units in the process of transforming resources (inputs) into desired results (outputs). The input indicators are the education expenditure and student/teacher ratio; the output indicators are the standard deviations of scores and percentage of resilient students. The standard deviation of the scores can explain how skills and knowledge are distributed throughout the student population, and the percentage of resilient students indicates to what extent the socioeconomic background precludes or does not preclude student success.
  PubDate: 2022-12-20
  DOI: 10.15388/LJS.2022.29667
  Issue No: Vol. 61 (2022)
Filling the gap in food and nutrition security data: What imputation
method is best for Africa's food and nutrition security'
- Authors: Adusei Bofa, Temesgen Zewotir
  Pages: 16–3 - 16–3
  Abstract: Our study presents the methods adopted to produce accurate imputed values for Africa's food security and nutrition (FSN). We focused primarily on the following five imputation methods for handling missing data: Mean Imputation; Multiple Imputed values using a Chained Equation (MICE); imputation based on the Conditional Distribution of a variable Diagnostics (mi); Additive Regression and Predictive Mean Matching (Hmisc); and Random Forest (missForest). We describe each method, including how they performed under MAR and MNAR using RMSE and MAE as a measure of accuracy. After these methods of imputation were examined for nonignorable missing values in the context of accurate and unbiased estimates for food and security analysis, we found that MissForest handled nonignorable missing values more effectively and with less bias, increasing the precision of the data by imputing the closest data values within the dataset. Hence the missForest is the best alternative for handling missing values for food security and nutrition concerning Africa. This study adds to the current body of knowledge on food and nutrition insecurity and provides useful information to policymakers, particularly about the imputations of missing values aimed at food security and nutrition concerning Africa, which has significant economic and social ramifications.
  PubDate: 2022-12-20
  DOI: 10.15388/LJS.2022.31573
  Issue No: Vol. 61 (2022)
Discriminating poetry and prose using syllable statistics
- Authors: Gediminas Murauskas, Marijus Radavičius
  Pages: 32–4 - 32–4
  Abstract: The aim of the paper is to construct a universal classifier to discriminate short Lithuanian text excerpts of poetry from that of prose. Here the universality means that the classifier is relatively insensitive to a text content and author's style. Since syllables represent phonetic properties and are less sensitive to text content as compared to words, the classifier training is based on frequencies of syllables in texts to be classified. The text data is taken from digitized library http://ebiblioteka.mkp.emokykla.lt. The error rate of the trained classifier applied to testing excerpts of 100 words is less than 5\%.
  PubDate: 2022-12-20
  DOI: 10.15388/LJS.2022.31988
  Issue No: Vol. 61 (2022)