The analysis of insurance and annuity products issued on multiple lives requires the use of statistical models which account for lifetime dependence. This paper presents a Dirichlet process mixture-based approach that allows to model dependent lifetimes within a group, such as married couples, accounting for individual as well as group-specific covariates. The model is analyzed in a fully Bayesian setting and illustrated to jointly model the lifetime of male–female couples in a portfolio of joint and last survivor annuities of a Canadian life insurer. The inferential approach allows to account for right censoring and left truncation, which are common features of data in survival analysis. The model shows improved in-sample and out-of-sample performance compared to traditional approaches assuming independent lifetimes and offers additional insights into the determinants of the dependence between lifetimes and their impact on joint and last survivor annuity prices.

Who joins extremist movements? Answering this question is beset by methodological challenges as survey techniques are infeasible and selective samples provide no counterfactual. Recruits can be assigned to contextual units, but this is vulnerable to problems of ecological inference. In this article, we elaborate a technique that combines survey and ecological approaches. The Bayesian hierarchical case–control design that we propose allows us to identify individual-level and contextual factors patterning the incidence of recruitment to extremism, while accounting for spatial autocorrelation, rare events, and contamination. We empirically validate our approach by matching a sample of Islamic State (ISIS) fighters from nine MENA countries with representative population surveys enumerated shortly before recruits joined the movement. High-status individuals in their early twenties with college education were more likely to join ISIS. There is more mixed evidence for relative deprivation. The accompanying extremeR package provides functionality for applied researchers to implement our approach.

Evolutionary studies on Dengue virus (DENV) in endemic regions are necessary since naturally occurring mutations may lead to genotypic variations or shifts in serotypes, which may lead to future outbreaks. Our study comprehends the evolutionary dynamics of DENV, using phylogenetic, molecular clock, skyline plots, network, selection pressure, and entropy analyses based on partial CprM gene sequences. We have collected 250 samples, 161 in 2017 and 89 in 2018. Details for the 2017 samples were published in our previous article and that of 2018 are presented in this study. Further evolutionary analysis was carried out using 800 sequences, which incorporate the study and global sequences from GenBank: DENV-1 (n = 240), DENV-3 (n = 374), and DENV-4 (n = 186), identified during 1944–2020, 1956–2020, and 1956–2021, respectively. Genotypes V, III, and I were identified as the predominant genotypes of the DENV-1, DENV-3, and DENV-4 serotypes, respectively. The rate of nucleotide substitution was found highest in DENV-3 (7.90 × 10−4 s/s/y), followed by DENV-4 (6.23 × 10−4 s/s/y) and DENV-1 (5.99 × 10−4 s/s/y). The Bayesian skyline plots of the Indian strains revealed dissimilar patterns amongst the population size of the three serotypes. Network analyses showed the presence of different clusters within the prevalent genotypes. The data presented in this study will assist in supplementing the measures for vaccine development against DENV.

Ageism has become a social problem in an aged society. This study re-examines an ageism affirmation strategy; the designs and plans for this study were pre-registered. Participants were randomly assigned to either an experimental group (in which they read an explanatory text about the stereotype embodiment theory and related empirical findings) or a control group (in which they read an irrelevant text). The hypothesis was that negative attitudes toward older adults are reduced in the experimental group compared with the control group. Bayesian analysis was used for hypothesis testing. The results showed that negative attitudes toward older adults were reduced in the experimental group. These findings contribute to the development of psychological and gerontological interventions aimed at affirming ageism. In addition, continued efforts to reduce questionable research practices and the spread of Bayesian analysis in psychological research are expected.

This paper compares the behavior of individuals playing a classic two-person deterministic prisoner’s dilemma (PD) game with choice data obtained from repeated interdependent security prisoner’s dilemma games with varying probabilities of loss and the ability to learn (or not learn) about the actions of one’s counterpart, an area of recent interest in experimental economics. This novel data set, from a series of controlled laboratory experiments, is analyzed using Bayesian hierarchical methods, the first application of such methods in this research domain.

We find that individuals are much more likely to be cooperative when payoffs are deterministic than when the outcomes are probabilistic. A key factor explaining this difference is that subjects in a stochastic PD game respond not just to what their counterparts did but also to whether or not they suffered a loss. These findings are interpreted in the context of behavioral theories of commitment, altruism and reciprocity. The work provides a linkage between Bayesian statistics, experimental economics, and consumer psychology.

Birnbaum and Quispe-Torreblanca (2018) presented a frequentist analysis of a family of six True and Error (TE) models for the analysis of two choice problems presented twice to each participant. Lee (2018) performed a Bayesian analysis of the same models, and found very similar parameter estimates and conclusions for the same data. He also discussed some potential differences between Bayesian and frequentist analyses and interpretations for model comparisons. This paper responds to certain points of possible controversy regarding model selection that attempt to take into account the concept of flexibility or complexity of a model. Reasons to question the use of Bayes factors to decide among models differing in fit and complexity are presented. The partially nested inter-relations among the six TE models are represented in a Venn diagram. Another view of model complexity is presented in terms of possible sets of data that could fit a model rather than in terms of possible sets of parameters that do or do not fit a given set of data. It is argued that less complex theories are not necessarily more likely to be true, and when the space of all possible theories is not well-defined, one should be cautious in interpreting calculated posterior probabilities that appear to prove a theory to be true.

Sixty-two 14C dates are analyzed in combination with a recently established local floating tree-ring sequence for the Early Neolithic site of La Draga (Banyoles, northeast Iberian Peninsula). Archaeological data, radiometric and dendrochronological dates, as well as sedimentary and micro-stratigraphical information are used to build a Bayesian chronological model, using the ChronoModel 2.0 and OxCal 4.4 computer programs, and IntCal 2020 calibration curve. The dendrochronological sequence is analyzed, and partially fixed to the calendrical scale using a wiggle-matching approach. Depositional events and the general stratigraphic sequence are expressed in expanded Harris Matrix diagrams and ordered in a temporal sequence using Allen Algebra. Post-depositional processes affecting the stratigraphic sequence are related both to the phreatic water level and the contemporaneous lakeshore. The most probable chronological model suggests two main Neolithic occupations, that can be divided into no less than three different “phases,” including the construction, use and repair of the foundational wooden platforms, as well as evidence for later constructions after the reorganization of the ground surface using travertine slabs. The chronological model is discussed considering both the modern debate on the Climatic oscillations during the period 8000–4800 cal BC, and the origins of the Early Neolithic in the western Mediterranean region.

Modelling loss reserve data in run-off triangles is challenging due to the complex but unknown dynamics in the claim/loss process. Popular loss reserve models describe the mean process through development year, accident year, and calendar year effects using the analysis of variance and covariance (ANCOVA) models. We propose to include in the mean function the persistence terms in the conditional autoregressive range model for modelling the persistence of claim across development years. In the ANCOVA model, we adopt linear trends for the accident and calendar year effects and a quadratic trend for the development year effect. We investigate linear or log-transformed mean functions and four distributions, namely generalised beta type 2, generalised gamma, Weibull, and exponential extension, with positive support to enhance the model flexibility. The proposed models are implemented using the Bayesian user-friendly package Stan running in the R environment. Results show that the models with log-transformed mean function and persistence terms provide better model fits. Lastly, the best model is applied to forecast partial loss reserve and calendar year reserve for three years.

In the present study, I explored the relationship between people's trust in different agents related to the prevention of the spread of coronavirus disease 2019 (COVID-19) and their compliance with pharmaceutical and non-pharmaceutical preventive measures. The COVIDiSTRESSII Global Survey dataset, which was collected from international samples, was analysed to examine the aforementioned relationship across different countries. For data-driven exploration, network analysis and Bayesian generalised linear model (GLM) analysis were performed. The result from network analysis demonstrated that trust in the scientific research community was most central in the network of trust and compliance. In addition, the outcome from Bayesian GLM analysis indicated that the same factor, trust in the scientific research community, was most fundamental in predicting participants' intent to comply with both pharmaceutical and non-pharmaceutical preventive measures. I briefly discussed the implications of the findings, the importance of trust in the scientific research community in explaining people's compliance with a measure to prevent the spread of COVID-19.

Wiggle matching is an important and powerful technique in radiocarbon dating that can be used to improve the precision of calendar age estimates. All radiocarbon determinations require calibration to provide calendar age estimates. This calibration is achieved by comparing the determinations against a calibration curve $\mu ( \cdot )$ to calculate the probability the sample arises from any particular calendar age t. Wiggle matching involves the calibration of a set of radiocarbon determinations taken from samples with known separations between their calendar ages. Since the calendar age separations between samples are known, all the calendar ages are known functions of one particular age, ${T_1}$ — commonly the most recent calendar age. Dating the sequence then reduces to considering $p({T_1} = {t_1}|data)$, the probability of the calendar age ${t_1}$ given the set of radiocarbon determinations. In previous work, a Bayesian approach has been used to derive a nice formula for this quantity under the assumption we have independent pointwise estimates of the calibration curve $\mu (t)$. In this paper, we derive a generalization of this formula showing how to incorporate covariance information from the calibration curve under an assumption of multivariate normality.

The paper explores the emergence and development of arable farming in southeastern Norway by compiling and analyzing directly dated cereals from archaeological contexts. By using summed probability distributions of radiocarbon dates and Bayesian modeling, the paper presents the first comprehensive analysis of the directly dated evidence for farming in the region. The models provide a more precise temporal resolution to the development than hitherto presented. The results demonstrate that the introduction of arable farming to southeastern Norway was a long-term development including several steps. Three different stages are pointed out as important in the process of establishing arable farming: the Early and Middle Neolithic, the Late Neolithic, and the Early Iron Age.

Research in bilingualism often involves quantifying constructs of interest by the use of rating scales: for example, to measure language proficiency, dominance, or sentence acceptability. However, ratings are a type of ordinal data, which violates the assumptions of the statistical methods that are commonly used to analyse them. As a result, the validity of ratings is compromised and the ensuing statistical inferences can be seriously distorted. In this article, we describe the problem in detail and demonstrate its pervasiveness in bilingualism research. We then provide examples of how bilingualism researchers can employ an appropriate solution using Bayesian ordinal models. These models respect the inherent discreteness of ratings, easily accommodate non-normality, and allow modelling unequal psychological distances between response categories. As a result, they can provide more valid, accurate, and informative inferences about graded constructs such as language proficiency. Data and code are publicly available in an OSF repository at https://osf.io/grs8x.