We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.Comment: Published at http://dx.doi.org/10.1214/15-AOS1371 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Supervised and semi-supervised source separation algorithms based on non-negative matrix factorization have been shown to be quite effective. However, they require isolated training examples of one or more sources, which is often difficult to obtain. This limits the practical applicability of these algorithms. We examine the problem of efficiently utilizing general training data in the absence of specific training examples. Specifically, we propose a method to learn a universal speech model from a general corpus of speech and show how to use this model to separate speech from other sound sources. This model is used in lieu of a speech model trained on speaker-dependent training examples, and thus circumvents the aforementioned problem. Our experimental results show that our method achieves nearly the same performance as when speaker-dependent training examples are used. Furthermore, we show that our method improves performance when training data of the non-speech source is available.

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BACKGROUND Evidence of racial/ethnic inequalities in tobacco outlet density is limited by: (1) reliance on studies from single counties or states, (2) limited attention to spatial dependence, and (3) an unclear theory-based relationship between neighborhood composition and tobacco outlet density. METHODS In 97 counties from the contiguous US, we calculated the 2012 density of likely tobacco outlets (N=90,407), defined as tobacco outlets per 1,000 population in census tracts (n=17,667). We used two spatial regression techniques, (1) a spatial errors approach in GeoDa software and (2) fitting a covariance function to the errors using a distance matrix of all tract centroids. We examined density as a function of race, ethnicity, income, and two indicators identified from city planning literature to indicate neighborhood stability (vacant housing, renter-occupied housing). RESULTS The average density was 1.3 tobacco outlets per 1,000 persons. Both spatial regression approaches yielded similar results. In unadjusted models, tobacco outlet density was positively associated with the proportion of Black residents and negatively associated with the proportion of Asian residents, White residents and median household income. There was no association with the proportion of Hispanic residents. Indicators of neighborhood stability explained the disproportionate density associated with Black residential composition, but inequalities by income persisted in multivariable models. CONCLUSIONS Data from a large sample of US counties and results from two techniques to address spatial dependence strengthen evidence of inequalities in tobacco outlet density by race and income. Further research is needed to understand the underlying mechanisms in order to strengthen interventions.

Much of what is known about neighborhood variation in the price of combustible tobacco products focuses on premium brand cigarettes. The current study extends this literature in two ways, by studying prices for the cheapest cigarette pack regardless of brand and a popular brand of flavored cigarillos and by reporting data from the largest statewide sample of licensed tobacco retailers. Significantly lower prices in neighborhoods with a higher proportion of youth and of racial/ethnic groups with higher smoking prevalence are a cause of concern. The study results underscore the need for policies that reduce availability and increase price of combustible tobacco products, particularly in states with low, stagnant tobacco taxes.

Purpose-The purpose of this paper is to provide an example of Lean Six Sigma (LSS) application in research and development (R&D) organizations to eliminate waste and improve systems based on available data that in turn improves the innovative environment. Manufacturing R&D involves designing and testing innovative concepts and taking them into high-volume manufacturing. The infrastructure associated with such organizations involves experimental manufacturing lines with the ability to evaluate the result under statistical process control and configuration control. The integration of LSS process improvement methodology into the R&D organization infrastructure and operations can have a dramatic effect on reducing cost and time related to the development and delivery of new technologies and products. Design/methodology/approach-The LSS methodology was systematically implemented to eliminate waste and improve the existing process of Intel's configuration control during the development and ramp phases. The steps included an assessment of the current state through walking the process and collecting baseline data, preparing the process map to quantify waste and inefficiencies, defining the ideal state along with a realistic target, selecting and implementing the improvement actions together with realizing and documenting the improvements and finally developing and putting into place a control plan to ensure the new process is sustained. The LSS approach resulted in an efficiency improvement exceeding the target, i.e. 60 per cent reduction in idle time and waste (non-value-added activities) versus a target of 40 per cent reduction. The results also showed an increase in the stakeholder satisfaction without compromising the technical rigor of the manufacturing configuration control. Findings-The LSS case study presented in this paper provides experiences to LSS practitioners in manufacturing R&D environment where the operational excellence is to be sought in new technology and product development. Originality/value-Project leaders can use the study to help formulate strategies to cater to customer/ stakeholder satisfaction and eliminate waste while maintaining the technical rigor of the R&D environment.

Feedback has a powerful influence on learning, but it is also expensive to provide. In large classes it may even be impossible for instructors to provide individualized feedback. Peer assessment is one way to provide personalized feedback that scales to large classes. Besides these obvious logistical benefits, it has been conjectured that students also learn from the practice of peer assessment. However, this has never been conclusively demonstrated. Using an online educational platform that we developed, we conducted an in-class matched-set, randomized crossover experiment with high power to detect small effects. We establish that peer assessment causes a small but significant gain in student achievement. Our study also demonstrates the potential of web-based platforms to facilitate the design of high-quality experiments to identify small effects that were previously not detectable.

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