Theoretical results on the discrete Weibull distribution of Nakagawa and Osaki

In this paper, we discuss some theoretical results and properties of the discrete Weibull distribution, which was introduced by Nakagawa and Osaki [The discrete Weibull distribution. IEEE Trans Reliab. 1975;24:300–301]. We study the monotonicity of the probability mass, survival and hazard functions. Moreover, reliability, moments, p-quantiles, entropies and order statistics are also studied. We consider likelihood-based methods to estimate the model parameters based on complete and censored samples, and to derive confidence intervals. We also consider two additional methods to estimate the model parameters. The uniqueness of the maximum likelihood estimate of one of the parameters that index the discrete Weibull model is discussed. Numerical evaluation of the considered model is performed by Monte Carlo simulations. For illustrative purposes, two real data sets are analyzed.     

PEER PRESSURE: EXPERIMENTAL EVIDENCE FROM RESTROOM BEHAVIOR

We provide experimental evidence on the effect of peer pressure on individual behavior. Specifically, we study the effect of being exposed to an observer in a public restroom on handwashing and urinal flushing behavior. Our estimates show that being exposed to an observer increases the probability of handwashing by 13 percentage points and the probability of urinal flushing by 15 percentage points. Given that handwashing and urinal flushing have social benefits that exceed individual benefits, our findings provide support for peer pressure as an additional way of addressing the social suboptimality arising from externalities. (JEL C91, C93)     

Annualized hours as a capacity planning tool in make-to-order or assemble-to-order environment: An agricultural implements company case

Capacity planning is one of the most important elements for an efficient production planning and control system. It is even more important when the main strategies to fulfil sales requirements are make-to-order (MTO) or assemble-to-order (ATO). Neither MTO or ATO companies keep finished products in stock which means that production level is driven by actual sales figures. Therefore, it is very important to have an available capacity as flexible as possible to meet sales requirements. Annualized hours (AH) is a work time control system that helps in increasing the flexibility of available capacity. The main rule of the AH system is to hire employees to work a certain number of hours on a yearly basis. Following some other pre-defined rules and limits, the AH system allows a company to adjust weekly available time in order to reduce idle capacity or to fulfil requirements that could mean overtime. This paper proposes a linear programming model to plan the operations using AH. This model has been implemented in a company that produces agricultural implements, showing that it is very simple to use. It is producing excellent practical results.     

Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems

Most system identification approaches and statistical inference methods rely on the availability of the analytic knowledge of the probability distribution function of the system output variables. In the case of dynamic systems modelled by hidden Markov chains or stochastic nonlinear state-space models, these distributions as well as that of the state variables themselves, can be unknown or untractable. In that situation, the usual particle Monte Carlo filters for system identification or likelihood-based inference and model selection methods have to rely, whenever possible, on some hazardous approximations and are often at risk. This review shows how a recent nonparametric particle filtering approach can be efficiently used in that context, not only for consistent filtering of these systems but also to restore these statistical inference methods, allowing, for example, consistent particle estimation of Bayes factors or the generalisation of model parameter change detection sequential tests. Real-life applications of these particle approaches to a microbiological growth model are proposed as illustrations.     

New Prediction Interval and Band in the Nonlinear Regression Model: Application to Predictive Modeling in Foods

This article is concerned with the proposal of a new prediction interval and band for the nonlinear regression model. The construction principle of this interval and band is based on an exact (the meaning of the term “exact” will be given later) confidence region for parameters of the nonlinear regression model. This region, fully described in Vila and Gauchi (2007 Vila , J.-P. , Gauchi , J.-P. ( 2007 ). Optimal designs based on exact confidence regions for parameter estimation of a nonlinear regression model . Journal of Statistical Planning and Inference 137 ( 9 ): 2935 – 2953 .[Crossref], [Web of Science ®] , [Google Scholar]), provides a rigorous justification for the new prediction interval and band that we propose. This new band is then compared to the classical bands (which are asymptotic and thus approximate for small n), and also to the band based on the bootstrap resampling method. The comparison of these bands is undertaken with simulated and real data from predictive modeling in food science.     

Toward Defining the Causal Role of Consciousness: Using Models of Memory and Moral Judgment from Cognitive Neuroscience to Expand the Sociological Dual‐Process Model

What role does “discursive consciousness” play in decision‐making? How does it interact with “practical consciousness?” These two questions constitute two important gaps in strong practice theory that extend from Pierre Bourdieu's habitus to Stephen Vaisey's sociological dual‐process model and beyond. The goal of this paper is to provide an empirical framework that expands the sociological dual‐process model in order to fill these gaps using models from cognitive neuroscience. In particular, I use models of memory and moral judgment that highlight the importance of executive functions and semantic memory. I outline each model as it pertains to the aforementioned gaps in strong practice theory. I then use the models from cognitive neuroscience to create an expanded dual‐process model that addresses how and when conscious mental systems override and interact with subconscious mental systems in the use of cultural ends for decision‐making. Finally, using this expanded model I address the sociological debate over the use of interview and survey data. My analysis reveals that surveys and interviews both elicit information encoded in declarative memory and differ primarily in the process of information retrieval that is required of respondents.     

NARRATIVE IDENTITIES:

On the U.S.-Mexican border, the label "Mexican' refers simultaneously to both nationality and ethnicity. Playing with the multiple meanings of the word, border actors construct narratives about themselves and Others to deal with a local hegemonic discourse that states that "poverty is Mexican.' Thus, while border identities are constructed within a culturally specific system of classification, people also develop a sense of themselves as subjects by imagining themselves as protagonists in stories. To know border narratives is important because people generally act according to how they understand their place in any number of social relations whose meaning is narratively constructed.     

The Moral Economy of Neoliberalism: How Voters Use Neoliberal Ideology to (De)Legitimate Undocumented Worker Access to Labor Markets

Neoliberalism is prevalent in American life. While researchers have documented the use of neoliberal ideology in institutional and macrolevel policy contexts, they have yet to investigate how voters use neoliberal ideology to legitimate their position on economic policy. I use data from semi‐structured interviews with 85 Tucsonans about why they voted the way they did on Proposition 202 (2008): “Arizona Stop Illegal Hiring”—which sought to reregulate undocumented worker labor market access—to address this gap. I found evidence of two distinct neoliberal ideological legitimations: “fair market competition” and “individual responsibility.” Furthermore, I use these data to shed light on the debate over whether neoliberalism spans partisan affiliation or converges with American conservatism. I found that voters across party lines who supported the measure paired neoliberal legitimations with conservative legitimations. We can interpret this bipartisan use of neoliberal ideology as evidence of a neoliberal “moral economy”—or consensus about the moral principles in which market action is embedded. Evidence of this moral economy indicates that moral principles from neoliberal ideology are simultaneously bipartisan and converge with American conservatism. These findings suggest that there could be a broader moral consensus among voters concerning the legitimacy of anti‐immigration economic policies.     

Legalization and human capital accumulation

This paper presents new evidence regarding the effects of legalization on the training of immigrants who were granted legal status through the US Immigration Reform and Control Act (IRCA) of 1986. Our findings point to a large increase in the immigrants’ incidence of training relative to comparable groups of natives following legalization. While training gains are higher for males, wage gains are higher for females. We also show that an important part of these changes in labor market outcomes occurs through occupation changes by newly legalized immigrants.