Using the Concepts of Hermeneutical Injustice and Ideology to Explain the Stability of Ancient Egypt During the Middle Kingdom

This paper argues that the relative stability of ancient Egyptian society during the Middle Kingdom (c.2055 – 1650 BC) can in part be explained by referring to the phenomenon of hermeneutical injustice, i.e., the manner in which imbalances in socio-economic power are causally correlated with imbalances in the conceptual scheme through which people attempt to interpret their social reality and assert their interests in light of their interpretations. The court literature of the Middle Kingdom is analyzed using the concepts of hermeneutical injustice and ideology. It is argued that while it is true that there was room for maneuver and for internal critique, the efficacy of internal critique was hindered by the structure of the intellectual discourse of Middle Kingdom Egypt. This intellectual discourse was suitable for the interpretation of social reality in a way that allowed the elites to assert their interests, but it was not suitable for the interpretation of social reality in a way that accorded with the interests of the exploited peasantry.     

Guided Censored Regression

Parametrically guided non‐parametric regression is an appealing method that can reduce the bias of a non‐parametric regression function estimator without increasing the variance. In this paper, we adapt this method to the censored data case using an unbiased transformation of the data and a local linear fit. The asymptotic properties of the proposed estimator are established, and its performance is evaluated via finite sample simulations.     

Bayesian non-crossing quantile regression for regularly varying distributions

Quantile regression is a very important statistical tool for predictive modelling and risk assessment. For many applications, conditional quantile at different levels are estimated separately. Consequently the monotonicity of conditional quantiles can be violated when quantile regression curves cross each other. In this paper, we propose a new Bayesian multiple quantile regression based on heavy tailed distribution for non-crossing. We consider a linear quantile regression model for simultaneous Bayesian estimation of multiple quantiles based on a regularly varying assumptions. The numerical and competitive performance of the proposed method is illustrated by simulation.     

Two-Term Edgeworth Expansions for the Classes of U- and V-statistics

Much effort has been devoted to deriving Edgeworth expansions for various classes of statistics that are asymptotically normally distributed, with derivations tailored to the individual structure of each class. Expansions with smaller error rates are needed for more accurate statistical inference. Two such Edgeworth expansions are derived analytically in this paper. One is a two-term expansion for the standardized U-statistic of order m, m ? 3, with an error rate o(n^{? 1}). The other is an expansion with the same error rate for the distribution of the standardized V-statistic of the same order. In deriving the Edgeworth expansion, we made use of the close connection between the V- and U-statistics, which permits to first derive the needed expansion for the related U-statistic, then extend it to the V-statistic, taking into consideration the estimation of all difference terms between the two statistics.     

Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors

Outbreaks of contagious diseases underscore the ever‐looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic‐induced workforce absenteeism. In particular, we develop a dynamic input‐output model capable of generating sector‐disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.     

Nonhomogeneous poisson processes as overhaul models

Hollander and Proschan (1974) studied nonhomogeneous Poisson processes as models for systems subject to overhauls. They did not postulate a functional form for the intensity, but showed that certain basic assumptions about the deterioration of the system implied that the mean function is superadditive. They studied tests of the null hypothesis that the intensity is constant with the alternative restricted to superadditive mean functions. For estimation purposes, the class of superadditive mean functions is too broad. We assume that the intensity is nondecreasing between overhauls and that at an overhaul it does not fall below its average prior to the overhaul. These two assumptions imply that the mean function is star-shaped. We obtain the restricted maximum-likelihood estimates under these two assumptions and under the star-shaped restriction. The two estimates are compared on a data set.     

Multiple belongings: transnational mobility,social class,and gendered identities among Polish migrants in Norway

ABSTRACT

Using ethnographic research in Norway and in Poland, this article focuses on the dynamics of multiple belongings of Polish migrants. It explores their experiences of belonging in relation to social class, gendered identities, and their different strategies of transnational mobility between Poland and Norway. By approaching belonging ‘from below’, we posit that it is a dynamic, processual, and socially and culturally constructed attachment to places, times, and communities, which includes experiential, practical, and affective dimensions. Considering the importance of questions of belonging and home-making in migrants’ lives, always contextually produced and read through performative reiterations, we focus on migrants’ daily routines and migratory practices, and argue that belonging is a multifaceted process, which takes on diverse forms and meanings of ‘who’ belongs to ‘what’, ‘where’ and ‘when’. Following intersectional perspective, the article aims at problematizing dependencies between mobility, gender, class, and migrants’ multiple belongings, and thereby, enhancing the understanding of the notion of belonging and its embeddedness in the inter-related social, cultural, economic, and political realms.     

A Non‐parametric ANOVA‐type Test for Regression Curves Based on Characteristic Functions

This article studies a new procedure to test for the equality of k regression curves in a fully non‐parametric context. The test is based on the comparison of empirical estimators of the characteristic functions of the regression residuals in each population. The asymptotic behaviour of the test statistic is studied in detail. It is shown that under the null hypothesis, the distribution of the test statistic converges to a finite combination of independent chi‐squared random variables with one degree of freedom. The coefficients in this linear combination can be consistently estimated. The proposed test is able to detect contiguous alternatives converging to the null at the rate n ^{? 1 ∕ 2}. The practical performance of the test based on the asymptotic null distribution is investigated by means of simulations.