How Much Should We Trust Differences-In-Differences Estimates?
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors...
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Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors...
Abstract Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, ...
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper provides a theoretical framework for analysis of consensus algorithms...
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well ...
Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion base...
This is a collection of articles dealing with the point of view of symbolic interactionism and with the topic of methodology in the discipline of sociology. It is written by the...
Wireless technology is a truly revolutionary paradigm shift, enabling multimedia communications between people and devices from any location. It also underpins exciting applicat...
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following...
In this article we offer a view that suggests that a firm's critical resources may span firm boundaries and may be embedded in interfirm resources and routines. We argue that an...
Abstract We have designed two taxon‐selective primers for the internal transcribed spacer (ITS) region in the nuclear ribosomal repeat unit. These primers, ITS1‐F and ITS4‐B, we...