Market Segmentation and Dynamic Analysis of Sparkling Wine Purchases in Italy

The Italian market of sparkling wines increases as volume and assortment (such as brands, appellations, typologies) mainly because of sparkling Prosecco consumption. We investigate the repeated purchase behavior of sparkling wines in two years within the supermarket channel through scanner data collected from a consumer panel. We propose a Hidden Markov Model to analyze these data, assuming an unobservable process to capture consumers’ preferences and allowing us to consider purchases sparsity over time. We consider multivariate responses defining types of purchases, namely price, appellation, and sugar content. Customers’ covariates influence the initial and transition probabilities of the latent process. We identify five market segments, and we track their evolution over time. One segment includes Prosecco-oriented consumers, and we show that loyalty to Prosecco changes strongly over time according to the region of residence, income, and family type. The findings improve the understanding of the market and may provide evidence to design successful marketing strategies. (JEL Classifications: C33, C51, D12, L66)

The Challenge of Inferring Unconscious Mental Processes

Studies of unconscious mental processes often compare a performance measure (e.g., some assessment of perception or memory) with a measure of awareness (e.g., a verbal report or forced-choice response) of the critical cue or contingency taken either concurrently or separately. The resulting patterns of bivariate data across participants lend themselves to several analytic approaches for inferring the existence of unconscious mental processes, but it is rare for researchers to consider the underlying generative processes that might cause these patterns. We show that bivariate data are generally insufficient to discriminate single-process models, with a unitary latent process determining both performance and awareness, from dual-process models, comprising distinct latent processes for performance and awareness. Future research attempting to isolate and investigate unconscious processes will need to employ richer types of data and analyses.

How Spatial Attention Affects the Decision Process: Looking through the Lens of Bayesian Hierarchical Diffusion Model & EEG Analysis

Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.

How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of the well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.

Maximum likelihood inference of pathogen population size history from a phylogeny

ABSTRACTInference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. Non-parametric models for population dynamics combined with molecular clock models which relate genetic data to time have enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The theory for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the models to genetic data from several pathogen data sets.

Problem of quasi-boundaries in the Universe and its projection upon life

Modern science and culture reappraises the objectivistic-materialistic paradigm towards considering human and psychological factors in the worldview, which also leads to problematization of the concept of boundaries of the objects and phenomena in the Universe. Methodological tool for this research became the narrative-play paradigm based on the universal categories of plot, scenario, likeness, two-dimensionality and game, which generalizes the systemic approach, liberating it from a dangerous tendency towards objectivism and one-sided materialism; reveals the latent process of formation of shared life through interaction of individual perceptions, sensations, thoughts and actions. Leaning on the narrative-play methodology and polemics with the opponents, the author examines the concept of quasi-boundary (as there could be no absolute boundaries in the totally interconnected Universe), which determines the forms of material structures, as well as contradictory boundaries that appear to be the subject of various conflicts. Such approach expands the creative abilities of a person, contributing to the development of activity programs in various spheres of life: education, resolution of ethno-confessional issues, innovation management, personal growth, as well as solution of the crucial task that defines the fate of earth dwellers – upbringing each person in harmony of the spiritual and material, in understanding oneness of the Universe, their place within it, and personal responsibility for the shared present and future.

A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: a case study of rattiness in a low-income urban Brazilian community

A key requirement in studies of endemic vector-borne or zoonotic disease is an estimate of the spatial variation in vector or reservoir host abundance. For many vector species, multiple indices of abundance are available, but current approaches to choosing between or combining these indices do not fully exploit the potential inferential benefits that might accrue from modelling their joint spatial distribution. Here, we develop a class of multivariate generalized linear geostatistical models for multiple indices of abundance. We illustrate this novel methodology with a case study on Norway rats in a low-income urban Brazilian community, where rat abundance is a likely risk factor for human leptospirosis. We combine three indices of rat abundance to draw predictive inferences on a spatially continuous latent process, rattiness , that acts as a proxy for abundance. We show how to explore the association between rattiness and spatially varying environmental factors, evaluate the relative importance of each of the three contributing indices and assess the presence of residual, unexplained spatial variation, and identify rattiness hotspots. The proposed methodology is applicable more generally as a tool for understanding the role of vector or reservoir host abundance in predicting spatial variation in the risk of human disease.

Modeling the Cognitive Trajectory in CADASIL

Background: For developing future clinical trials in Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), it seems crucial to study the long-term changes of cognition. Objective: We aimed to study the global trajectory of cognition, measured by the Mini-Mental State Examination (MMSE) and the Mattis Dementia Rating Scale (MDRS), along the course of CADASIL. Methods: Follow-up data of 185 CADASIL patients, investigated at the French National Referral center CERVCO from 2003, were considered for analysis based on strict inclusion criteria. Assuming that the MMSE and the MDRS provide imprecise measures of cognition, the trajectory of a common cognitive latent process during follow-up was delineated using a multivariate latent process mixed model. After adjustment of this model for sex and education, the sensitivities of the two scales to cognitive change were compared. Results: Analysis of the cognitive trajectory over a time frame of 60 years of age showed a decrease of performances with aging, especially after age of 50 years. This decline was not altered by sex or education but patients who graduated from high school had a higher mean cognitive level at baseline. The sensitivities of MMSE and MDRS scales were similar and the two scales suffered from a ceiling effect and curvilinearity. Conclusion: These data support that cognitive decline is not linear and mainly occurs after the age of 50 years during the course of CADASIL. They also showed that MMSE and MDRS scales are hampered by major limitations for longitudinal studies.