Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing

Abstract. In the context of multivariate mean regression, we propose a new method to measure and estimate the inadequacy of a given parametric model. The measure is basically the missed fraction of variation after adjusting the best possible parametric model from a given family. The proposed approach is based on the minimum L ² ‐distance between the true but unknown regression curve and a given model. The estimation method is based on local polynomial averaging of residuals with a polynomial degree that increases with the dimension d of the covariate. For any d ≥ 1 and under some weak assumptions we give a Bahadur‐type representation of the estimator from which ‐consistency and asymptotic normality are derived for strongly mixing variables. We report the outcomes of a simulation study that aims at checking the finite sample properties of these techniques. We present the analysis of a dataset on ultrasonic calibration for illustration.     

Selection of the Ridge Parameter Using Mathematical Programming

Ridge regression solves multicollinearity problems by introducing a biasing parameter that is called ridge parameter; it shrinks the estimates and their standard errors in order to reach acceptable results. Selection of the ridge parameter was done using several subjective and objective techniques that are concerned with certain criteria. In this study, selection of the ridge parameter depends on other important statistical measures to reach a better value of the ridge parameter. The proposed ridge parameter selection technique depends on a mathematical programming model and the results are evaluated using a simulation study. The performance of the proposed method is good when the error variance is greater than or equal to one; the sample consists of 20 observations, the number of explanatory variables in the model is 2, and there is a very strong correlation between the two explanatory variables.     

The labour division of Italian couples after a birth: assessing the effect of unobserved heterogeneity

The results of previous studies on the effect of childbearing on both parents’ paid and unpaid work suffer from the difficulty related to the specification of latent variables that influence the relationship between reproductive behaviour of the couple and working activity. The aim of this study is to estimate the effect of transition to parenthood on the partners’ division of labour by accounting for latent variables, such as the bargaining process between partners and endogenous fertility decisions. In particular, this is the first time that our specific estimation strategy has been applied to determine the impact of childbearing on the division of household labour between partners taking into account the reverse causality effect between labour and fertility decisions. We use longitudinal data on married or cohabiting Italian couples provided by the Italian panel survey of the Generation and Gender Program. Our results are consistent with previous studies, and show that the birth of a child affects a woman’s unpaid labour strongly and positively and her paid labour negatively, while men’s work is mostly unaffected. Moreover, when partners hold traditional attitudes regarding gender roles and the family, the female partner is even more strongly affected by childbearing in terms of total amount of labour (paid plus unpaid). However, our sensitivity analysis shows that these results appear only when our correction strategy for the misspecification of latent variables is applied.

     

Food fights: irritating for social change among Auckland's alternative food initiatives

ABSTRACT

This article explores alternative food initiatives (AFI) and their performances of benign transgression. Through collaborative activist-and-academic-storytelling we tease apart the divergent practices of AFIs to question what mediates these performances in the grey area between conventional and alternative practice. Grounded examples of AFIs performing alternative economy and related acts of ‘irritant’ civil disobedience show how subverting normative practices of power and authority can catalyse social reproduction of difference, and tangibly alter the conventional food system.     

The seasonality of conception

Individual data on menstrual cycles of noncontracepting women living in Western countries were used in order to verify whether the biological seasonality of conception persists after sexual behavior is controlled for. Episodes of intercourse were recorded daily, and the time of ovulation was detected by a marker We find that the seasonality of conception changes with woman's age and frequency of episodes of sexual intercourse. In particular; for women aged 27-31 having only one act of intercourse during the six most fertile days of the menstrual cycle, the seasonality of fecundability is stronger In this age group in the Northern Hemisphere, if seasonality of acts of sexual intercourse is controlled, the monthly distribution of probability of conception is bimodal, with two maxima (September and January) and two minima (December and March). When unobserved characteristics of the couples are considered, this seasonal pattern of conception persists.     

Analysis of heat wave effects on health by using generalized additive model and bootstrap-based model selection

Summary.  It is known that high summer temperature may lead to worsening health conditions among fragile individuals within exposed populations. It is also argued that multiday patterns of high temperature—heat waves—may have relevant effects on health. We discuss the possible measures of intensities of heat waves to be included in a generalized additive model explaining the number of hospital admissions that occurred during summer months in Milan. The issue of variable selection is central to the analysis: a computational method is discussed which may help in assessing the robustness of the model selection method. Eventually, we obtain evidence supporting the relevance of heat waves in driving adverse health episodes.