A comment on “A theoretical basis for perturbation methods” by Krishnamurty Muralidhar and Rathindra Sarathy

Statistics and Computing -     

Evaluation of long-term health care services through a latent Markov model with covariates

We focus on the evaluation of the long-term health care services provided to elderly patients by nursing homes of four different health districts in the Umbria region (Italy). To this end, we analyze data coming from a longitudinal survey aimed at assessing several aspects of patient health conditions and develop an extended version of the latent Markov model with covariates, which allows us to deal with dropout and intermittent missing data patterns that are common in longitudinal studies. Maximum likelihood estimates are obtained by a two-step approach that allows for fast estimation of model parameters and prevents some drawbacks of the standard maximum likelihood method encountered in the presence of many response variables and covariates. In the application to the observed data, we show how to obtain indicators of the effectiveness of the health care services delivered by each health district, by means of a resampling procedure.     

A Unified View of Nonparametric Trend-Cycle Predictors Via Reproducing Kernel Hilbert Spaces

We provide a common approach for studying several nonparametric estimators used for smoothing functional time series data. Linear filters based on different building assumptions are transformed into kernel functions via reproducing kernel Hilbert spaces. For each estimator, we identify a density function or second order kernel, from which a hierarchy of higher order estimators is derived. These are shown to give excellent representations for the currently applied symmetric filters. In particular, we derive equivalent kernels of smoothing splines in Sobolev and polynomial spaces. The asymmetric weights are obtained by adapting the kernel functions to the length of the various filters, and a theoretical and empirical comparison is made with the classical estimators used in real time analysis. The former are shown to be superior in terms of signal passing, noise suppression and speed of convergence to the symmetric filter.     

China Brotsky,Sarah M. Eisinger,and Diane Vinokur-Kaplan: Shared Space and the New Nonprofit Workplace

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -     

Outlines of a Critical Sociology of Consumption: Beyond Moralism and Celebration

The 'new' sociology of consumption that emerged in the 1980s acknowledged that consumption is a significant cultural and social practice and not just a mere signifier of the pathological elements of contemporary societies. Arguably, this rehabilitation of consumption has overshot its mark and has led to an overly positive, complacent and celebratory rendering of consumption and to an overriding concern with the symbolic and communicative dimension of goods. Driven by the debates on globalization, political consumerism and the relation between consumption and citizenship, current work shows increased interest in the politics of consumption. The article explores in what directions the classic concern with the symbolic and communicative dimension of goods might be taken if it is more thoroughly grounded in an analysis of the politics of consumption and of the current reconfigurations of consumers and citizens. The overall aim is to outline a basis for a critical sociology of consumption.     

No Small Change: Process‐Oriented Play Therapy For Children of Separating Parents

This article looks at a process‐oriented play therapy for children adversely affected by parental separation. Process‐oriented play therapy is a therapeutic method that involves the therapist directly entering the ‘world of play’ with the child, by amplifying various modes of expression and helping underlying meaning to emerge, in order to help children access aspects of their life they feel they have no say in. One particular case has been used as an example, involving ‘Jim’ (pseudonym) and his mother, who attended the play therapy session.     

Optimal sequential Bayesian analysis for degradation tests

Degradation tests are especially difficult to conduct for items with high reliability. Test costs, caused mainly by prolonged item duration and item destruction costs, establish the necessity of sequential degradation test designs. We propose a methodology that sequentially selects the optimal observation times to measure the degradation, using a convenient rule that maximizes the inference precision and minimizes test costs. In particular our objective is to estimate a quantile of the time to failure distribution, where the degradation process is modelled as a linear model using Bayesian inference. The proposed sequential analysis is based on an index that measures the expected discrepancy between the estimated quantile and its corresponding prediction, using Monte Carlo methods. The procedure was successfully implemented for simulated and real data.