Residence choices of Hispanic neighborhoods in Nevada

A well-known characteristic of Hispanic in the U.S. is their tendency to concentrate their settlement area in distinct locations. This study merges aggregate data of localities with micro observations to estimate the joint decisions of residential location and homeownership choice for Hispanics. To address the possibility that the disturbances in the regression may be correlated within groups, we apply a bivarite probit framework clustered by localities (PUMAs), using the Public Use Micro Statistics of Census 2000 data for Nevada. The results suggests that Hispanics choose to live in Hispanic enclaves are characterized by lower income, less English fluency, lower educational attainments and recent migration. Assessing the interaction of the homeownership decisions and location choice, we find that these two residential decisions are simultaneously determined and they have significantly positive effects on each other

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea

Variations in developmental patterns across pragmatic features

Drawing on the findings of longitudinal studies in uninstructed contexts over the last two decades, this synthesis explores variations in developmental patterns across second language (L2) pragmatic features. Two synthesis questions were addressed: (a) What are the variations in developmental patterns across pragmatic features?, and (b) What are the potential explanations for the variations? In response to the first question, previous studies showed that L2 pragmatic development is a non-linear, dynamic process, with developmental paces varying across pragmatic features (Ortactepe, 2013; Taguchi, 2010, 2011, 2012; Warga & Scholmberger, 2007). These studies revealed that some aspects of pragmatic features (e.g., semantic strategies of speech acts) develop faster than others (e.g., lexical features such as mitigators). In response to the second question, three potential explanations were identified to account for the developmental variations: (a) language-related, (b) situation-dependent, and (c) learner-related explanations, with three subcategories for the language-related explanation: (a) the functions of pragmatic features, (b) the frequency of availability of target features, and (c) the similarity and difference between languages with respect to the target feature