Parametric Modal Regression with Error in Covariates

An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from simulation experiments and data from real-world applications to demonstrate their implementation and performance. These empirical examples illustrate the importance of adequately accounting for measurement error in the error-prone covariate when inferring the association between a response and covariates based on a modal regression model that is especially suitable for skewed and heavy-tailed response data.Comment: 15 pages, 3 figure

Bayesian Modal Regression based on Mixture Distributions

Compared to mean regression and quantile regression, the literature on modal regression is very sparse. We propose a unified framework for Bayesian modal regression based on a family of unimodal distributions indexed by the mode along with other parameters that allow for flexible shapes and tail behaviors. Following prior elicitation, we carry out regression analysis of simulated data and datasets from several real-life applications. Besides drawing inference for covariate effects that are easy to interpret, we consider prediction and model selection under the proposed Bayesian modal regression framework. Evidence from these analyses suggest that the proposed inference procedures are very robust to outliers, enabling one to discover interesting covariate effects missed by mean or median regression, and to construct much tighter prediction intervals than those from mean or median regression. Computer programs for implementing the proposed Bayesian modal regression are available at https://github.com/rh8liuqy/Bayesian_modal_regression.Comment: 34 pages, 14 figure

Analysis of high end tourism market in China -Targeting bespoke tour-New high end tourism

With the popularity and development of mass tourism, an increasing number of tourists are not satisfied by the mass tourism offer, but rather prefer to gain more personal and special experience from their travels. Although the high-end tourism (luxury tourism) has been well developed around the world, it is an emerging tourism industry in the Chinese market. The aim of this project is to review the existing theory in terms of high-end tourism in China, position the market characteristics, investigate existing problems in new high-end tourism and give some proposal for improvement. The study was realized using online social media survey with tourism professionals. Furthermore, the study includes the SWOT of running high-end tourism business and PEST analysis of the macro market in ChinaTras la creciente popularidad y el desarrollo del turismo de masas, los turistas no están satisfechos con la oferta de turismo de masas, sino que prefieren adquirir experiencia más personal y especial de sus viajes. Aunque el turismo del alto nivel (turismo de lujo) se ha desarrollado bien alrededor del mundo, se trata de un sector turístico emergente en el mercado chino. El objetivo de este proyecto es revisar la teoría en términos de turismo de alto nivel en China, comprender las características del mercado, investigar los problemas existentes en el nuevo turismo de alto nivel y dar algunas propuestas de mejora. El estudio se realizó mediante la encuesta de medios sociales con los profesionales del turismo. Además, el estudio incluye el análisis DAFO de dirigir el negocio del turismo de alto nivel y análisis PEST del mercado macro en China.Liu, Q. (2015). Analysis of high end tourism market in China -Targeting bespoke tour-New high end tourism. Universitat Politècnica de València. http://hdl.handle.net/10251/56026TFG

Advancements in Parametric Modal Regression

This dissertation considers statistical inference methods for parametric modal regression models. In Chapter 1, we motivate the mode as the measure of central tendency instead of the median or the mean with an example. Following the motivational example, we include an overview of existing modal regression models. Later, in the same chapter, we explain advantages of the parametric modal regression models over existing nonparametric modal regression models. In Chapter 2, we address issues in statistical inference brought in by data contaminated with measurement error. With measurement error in covariates, statistical inference methods designed for modal regression models with error-free covariates become inappropriate. We use an innovative Monte-Carlo based method to revise the original log-likelihood function that one uses in the absence of covariates measurement error. This revision leads to a new objective function adequately accounting for measurement error that one maximizes with respect to unknown parameters in the regression model. We also propose a model diagnostic method based on parametric bootstrap for the parametric modal regression with error in covariates. The proposed method for estimating regression parameters is applicable for any parametric modal regression models. However, there are only a handful of existing distributions that are suitable for the modal regression model for heavy-tailed response data. To allow for flexible modal regression, we propose a new unimodal distribution called flexible Gumbel distribution in Chapter 3. We present both frequentist and Bayesian inference methods for the flexible Gumbel distribution in the same chapter. Chapter 4 introduces the general unimodal distribution family that encompasses a range of unimodal asymmetric distributions and incorporates the flexible Gumbel distribution as a specific instance. Based on the general unimodal distribution family, we propose a unified framework for Bayesian modal regression that is well-suited for analyzing asymmetric and fat-tailed data. We propose the Gaussian process modal regression model in Chapter 5. Unlike the classic Gaussian process regression model where one assumes a Gaussian process for the conditional mean of the response, in our proposed Gaussian process regression model, we assume a Gaussian process for the conditional mode

A View from the Start: A Review of Inhibitory Control Training in Early Childhood

Young children’s capacity to monitor and control their thoughts and behaviors is influenced largely by inhibitory control, which grows rapidly during this age due to brain maturation. This capacity has important implications for children’s development, including academic and social outcomes, and has been shown to be influenced by culture and exposure to adverse life events such as poverty. Research suggests that this capacity, importantly, may be largely trainable, with appropriate training programs