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Because of China’s new wave of COVID-19 in May 2023, the issue of tackling COVID-19 misinformation remains relevant. Based on Lippmann’s theory of public opinion and agenda setting theory, this article aims to examine the concept of digital pseudo-identification as a type of logical fallacy that refers to supporting journalists’ opinions with ‘false’ arguments that lack factual evidence. To do so, the study applied computer-aided content analysis, as well as rhetorical and critical discourse analyses, to examine 400 articles related to four COVID-19 vaccines (‘Oxford-AstraZeneca’, ‘Pfizer-BioNTech’, ‘Sputnik V’ and ‘Sinovac’) published on the online versions of two major British and American mainstream media sources between August 2020 and December 2021. The results of the study show that journalists of the ‘The New York Times’ and ‘The Guardian’ used similar logical fallacies, including the opinions of pseudo-authorities and references to pseudo-statistics and stereotypes, which contributed to creating distorted representations of the COVID-19 vaccines and propagating online misinformation. The study also reveals political bias in both of the mainstream media sources, with relatively more positive coverage of the European vaccines than non-European vaccines. The findings have important implications for journalism and open up perspectives for further research on the concept of digital pseudo-identification in the humanities and social sciences.

The main problem in pricing variance, volatility, and correlation swaps is how to determine the evolution of the stochastic processes for the underlying assets and their volatilities. Thus, sometimes it is simpler to consider pricing of swaps by so-called pseudo-statistics, namely, the pseudo-variance, -covariance, -volatility, and -correlation. The main motivation of this paper is to consider the pricing of swaps based on pseudo-statistics, instead of stochastic models, and to compare this approach with the most popular stochastic volatility model in the Cox–Ingresoll–Ross (CIR) model. Within this paper, we will demonstrate how to value different types of swaps (variance, volatility, covariance, and correlation swaps) using pseudo-statistics (pseudo-variance, pseudo-volatility, pseudo-correlation, and pseudo-covariance). As a result, we will arrive at a method for pricing swaps that does not rely on any stochastic models for a stochastic stock price or stochastic volatility, and instead relies on data/statistics. A data/statistics-based approach to swap pricing is very different from stochastic volatility models such as the Cox–Ingresoll–Ross (CIR) model, which, in comparison, follows a stochastic differential equation. Although there are many other stochastic models that provide an approach to calculating the price of swaps, we will use the CIR model for comparison within this paper, due to the popularity of the CIR model. Therefore, in this paper, we will compare the CIR model approach to pricing swaps to the pseudo-statistic approach to pricing swaps, in order to compare a stochastic model to the data/statistics-based approach to swap pricing that is developed within this paper.

In this study, the performance of three different self organization feature map (SOFM) network models denoted as SOFM1, SOFM2, and SOFM3 having neighborhood shapes, namely, SquareKohonenful, LineKohonenful, and Diamond- Kohenenful, respectively, to predict the critical factor of safety (F_s) of a widely-used artificial slope subjected to earthquake forces was investigated and compared. For this purpose, the reported data sets by Erzin and Cetin (2012) [7], including the minimum (critical) F_s values of the artificial slope calculated by using the simplified Bishop method, were utilized in the development of the SOFM models. The results obtained from the SOFM models were compared with those obtained from the calculations. It is found that the SOFM1 model exhibits more reliable predictions than SOFM2 and SOFM3 models. Moreover, the performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed to evaluate the prediction capacity of the SOFM models developed. The study demonstrates that the SOFM1 model is able to predict the F_s value of the artificial slope, quite efficiently, and is superior to the SOFM2 and SOFM3.

The data obtained from case–control sampling may suffer from selection or reporting bias, resulting in biased estimation of the parameter(s) of interest by standard analysis of case–control data. In this work, the problem of this bias is dealt with by introducing the concept of reporting probability. Then, considering a reference sample from the source population, we obtain asymptotically unbiased estimate of the population parameters by fitting a pseudo-likelihood, assuming the exposure distribution in the population to be unknown and arbitrary. The proposed estimates of the model parameters follow asymptotically a normal distribution and are semiparametrically fully efficient. We motivate the need for such methodology by considering the analysis of spontaneous adverse drug reaction (ADR) reports in presence of reporting bias.

This book creates situations in which the zeta function, or other L-functions, appear in spectral-theoretic questions. It also connects pseudo-differential analysis, or quantization theory, to analytic number theory.

In this paper we generalize Besag's pseudo-likelihood function for spatial statistical models on a region of a lattice. The correspondingly defined maximum generalized pseudo-likelihood estimates (MGPLEs) are natural extensions of Besag's maximum pseudo-likelihood estimate (MPLE). The MGPLEs connect the MPLE and the maximum likelihood estimate. We carry out experimental calculations of the MGPLEs for spatial processes on the lattice. These simulation results clearly show better performances of the MGPLEs than the MPLE, and the performances of differently defined MGPLEs are compared. These are also illustrated by the application to two real data sets. [PUBLICATION ABSTRACT]