<abstract xmlns="http://www.w3.org/1999/xhtml">

In this research study, the authors obtained the preliminary evaluation of the impact detection of vaccinations against COVID-19 in the EU-27. The empirical basis of the study was the daily number of COVID-19 cases, vaccinations, hospitalisations, and deaths in the EU countries from March 2020 to March 2022. Rules of association were used to identify non-obvious associations between vaccinations against COVID-19 and cases of illness, hospitalisations, and deaths from COVID-19. The obtained results were used to cluster the EU countries by the level of vaccinations against COVID-19, cases of COVID-19, deaths from COVID, and COVID-19 hospitalisations for the EU member states. The K-means clustering method was used for cluster analysis. Hidden dependencies of the number of COVID-19 cases, the number of COVID-19 hospitalisations, and the number of COVID-19 deaths due to the number of vaccinations against COVID-19 by EU countries were revealed. It was established with a high probability that vaccination significantly affects the level of morbidity. For the first time, association rules were obtained, which are preliminary estimates of the relationship between the dynamics of vaccinations against COVID-19 and the dynamics of COVID-19 cases, COVID-19 hospitalisations, and deaths from COVID-19 in the EU. The results can be used to make beneficial decisions, for example, to regulate vaccination policies in individual EU countries, and predict the future consequences of the COVID-19 pandemic.
</abstract>

The Application of Association Rules to Detect the Effects of Vaccinations against Covid-19 in the EU-27. Preliminary Estimates

<abstract xmlns="http://www.w3.org/1999/xhtml">

The aim of this research was to study a nonparametric estimator of the density and mode function of a scalar response variable given a functional variable, when the observations are i.i.d. This proposed estimator is given by combining Missing At Random (MAR) with the local linear approach. Finally, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the local linear approach with MAR to the presence of even a small proportion of outliers in the data.
</abstract>

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper attempted using the prospect theory to explain overbidding in first price auctions. The standard outlook in the literature on auctions is that bidders overbid, but the probability weighting functions are nonlinear as in the prospect theory, so they not only tend to underweight the probabilities of winning the auction but also overweight, so that there are overbidders and underbidders. This paper proves that to some extent, non-linear weighting functions do explain overbidding the risk-neutral Nash equilibrium valuation (RNNE). Furthermore, coherent risk measures, such as certainty equivalent and translation invariance, were used to show loss aversion among bidders, and in line with the prospect theory, convexity was also confirmed with sub-additivity, monotonicity and with positive homogeneity.
</abstract>

The Prospect Theory and First Price Auctions: an Explanation of Overbidding

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Econometrics

Volume 27 (2023): Issue 1 (March 2023)

The Application of Association Rules to Detect the Effects of Vaccinations against Covid-19 in the EU-27. Preliminary Estimates

The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random

The Prospect Theory and First Price Auctions: an Explanation of Overbidding

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