Quantile regression with varying coefficients

Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models. We consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines. The proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm. The proposed Rao-score-type test that assesses the model against a linear model is also easy to implement. We provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic. Empirical results are also provided, including an application of the methodology to forced expiratory volume (FEV) data.Comment: Published at http://dx.doi.org/10.1214/009053606000000966 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the Divisibility of Trinomials by Maximum Weight Polynomials over F2

Divisibility of trinomials by given polynomials over finite fields has been studied and used to construct orthogonal arrays in recent literature. Dewar et al.\ (Des.\ Codes Cryptogr.\ 45:1-17, 2007) studied the division of trinomials by a given pentanomial over \F_2 to obtain the orthogonal arrays of strength at least 3, and finalized their paper with some open questions. One of these questions is concerned with generalizations to the polynomials with more than five terms. In this paper, we consider the divisibility of trinomials by a given maximum weight polynomial over \F_2 and apply the result to the construction of the orthogonal arrays of strength at least 3.Comment: 10 pages, 1 figur

Interactional Functions of Demonstratives in Korean and Japanese Conversation.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

A little bluebird told me : social media conversation effects on business outcomes-evidence from the movie industry

textIn this dissertation, I examine how online conversations as electronic word-of-mouth (eWOM) information via social media networks affect business outcomes. Using data from the movie industry, my goal is to show how conversation quantity and quality, defined here as volumes and valence, on social network sites affect important business outcomes such as sales. Using a dynamic simultaneous equation system, I find that social media conversations can be a precursor to and an outcome of sales. Aggregated data from multiple sources show how social media variables and other key variables—volume, valence, and other information related to movies such as YouTube views, ratings, advertising, production budget, number of screens—contribute to box-office and home video sales through eWOM via social media. Findings highlight that eWOM volume correlates with box-office performance and home video sales: the more positive and strong the conversation, the higher the box office and home video sales. The study extends prior research on WOM and offers insight into how film studios can strategically manage social media to enhance box office and home video revenue.Advertisin