(Sub) Optimality and (non) optimal satisficing in risky decision experiments

We implement a risky choice experiment based on one-dimensional choice variables and risk neutrality induced via binary lottery incentives. Each participant confronts many parameter constellations with varying optimal payoffs. We assess (sub)optimality, as well as (non)optimal satisficing by eliciting aspirations in addition to choices. Treatments differ in the probability that a binary random event, which are payoff—but not optimal choice—relevant is experimentally induced and whether participants choose portfolios directly or via satisficing, i.e., by forming aspirations and checking for satisficing before making their choice. By incentivizing aspiration formation, we can test satisficing, and in cases of satisficing, determine whether it is optimal.     

Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model

In this paper, we assume that the duration of a process has two different intrinsic components or phases which are independent. The first is the time it takes for a trade to be initiated in the market (for example, the time during which agents obtain knowledge about the market in which they are operating and accumulate information, which is coherent with Brownian motion) and the second is the subsequent time required for the trade to develop into a complete duration. Of course, if the first time is zero then the trade is initiated immediately and no initial knowledge is required. If we assume a specific compound Bernoulli distribution for the first time and an inverse Gaussian distribution for the second, the resulting convolution model has a mixture of an inverse Gaussian distribution with its reciprocal, which allows us to specify and test the unobserved heterogeneity in the autoregressive conditional duration (ACD) model.

Our proposals make it possible not only to capture various density shapes of the durations but also easily to accommodate the behaviour of the tail of the distribution and the non monotonic hazard function. The proposed model is easy to fit and characterizes the behaviour of the conditional durations reasonably well in terms of statistical criteria based on point and density forecasts.     

Control‐based imputation for sensitivity analyses in informative censoring for recurrent event data

In clinical trials, missing data commonly arise through nonadherence to the randomized treatment or to study procedure. For trials in which recurrent event endpoints are of interests, conventional analyses using the proportional intensity model or the count model assume that the data are missing at random, which cannot be tested using the observed data alone. Thus, sensitivity analyses are recommended. We implement the control‐based multiple imputation as sensitivity analyses for the recurrent event data. We model the recurrent event using a piecewise exponential proportional intensity model with frailty and sample the parameters from the posterior distribution. We impute the number of events after dropped out and correct the variance estimation using a bootstrap procedure. We apply the method to an application of sitagliptin study.     

The Peasant World in the Discourse of Generational Sorrow

This article spells out the methodological considerations and various procedural and methodological subtleties developed over the author's quarter century of sociological field studies of Russia's peasant worlds. Particular attention is paid to “voices from below”—peasant discursive formats that capture, in natural and undiluted form, the evolution of peasant practices constituting the core of everyday life. In particular, the phenomenon of generational sorrow and a special chronological nostalgia that functions as a “social gyroscope” is identified and discursively captured. This phenomenon is noteworthy and useful for its ability to retain a record of the productively longed for past in a rural community's collective memory and thereby influence new peasant worlds taking shape today.     

Context and Scope of Social Work Interventions in Perinatal Mental Health Settings in India

The perinatal period may not be a happy time for all mothers. During this period, some may develop psychiatric complications, which have both biological and psychosocial etiology, and such illness may affect the whole family. Psychosocial aspects may trigger the illness and act as risk and maintaining factors. Social workers need to intervene in this context for optimal functioning of women with perinatal mental health issues and her family. A biopsychosocial framework may be adopted for social work assessment and intervention. Individual, familial, group, and community level intervention is required to address this complex issue, along with other mental health clinicians.