Seokho Lee

Understanding the Cultural Differences in Tourist Motivation Between Anglo-American and Japanese Tourists

Summary There is considerable evidence to suggest that differences in cultural characteristics exist across the world. Among them, individualistic societies emphasize “I” consciousness, autonomy, emotional independence, pleasure seeking and universalism. On the other hand, col-lectivistic societies stress “we” consciousness, collective identity, group solidarity, sharing, and particularism. A comparative research on the motivation of tourists from different cultures may challenge current tourism research, which mainly focuses on individualism and rationalism. These values of individualism and rationalism result in underestimation of the influence of groups, norms, culture, and emotion or impulse on tourist behavior. There have been few studies which attempt to directly measure cultural characteristics and identity across culture, and to explain how these cultural characteristics play a role in creating distinctive differences in tourist motivation. Thus, this study explores (1) cultural differences underlying individualism-collectivism between Anglo-American and Japanese tourists; (2) examines the relationship of two cultural dimensions to tourist motivation, and (3) suggests management implications facing tourism industry.

Messina Hof Wine and Jazz Festival: an economic impact analysis.

The purpose of the study was to estimate the economic impact on Brazos County, Texas, of a proposed weekend Wine and Jazz Festival to be produced by Messina Hof Winery. An economic impact analysis was performed using IMPLAN PRO software. The 1998 value of the US dollar and average weekend festival visitor expenditure data from Messina Hof Winery were used as inputs into the model. The results indicated that this single event would generate

Transfer of antibiotic resistance plasmids in pure and activated sludge cultures in the presence of environmentally representative micro-contaminant concentrations

892,981 in total sales output,

Sparse Principal Component Analysis for Identifying Ancestry-Informative Markers in Genome Wide Association Studies

324,942 in personal income and the equivalent of 21.8 jobs. The conclusion was that the weekend Wine and Jazz Festival proposed by Messina Hof Winery would have a significant positive economic impact on Brazos County.

A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

The presence of antibiotics in the natural environment has been a growing issue. This presence could also account for the influence that affects microorganisms in such a way that they develop resistance against these antibiotics. The aim of this study was to evaluate whether the antibiotic resistant gene (ARG) plasmid transfer can be facilitated by the impact of 1) environmentally representative micro-contaminant concentrations in ppb (part per billion) levels and 2) donor-recipient microbial complexity (pure vs. mixed). For this purpose, the multidrug resistant plasmid, pB10, and Escherichia coli DH5α were used as a model plasmid and a model donor, respectively. Based on conjugation experiments with pure (Pseudomonas aeruginosa PAKexoT) and mixed (activated sludge) cultures as recipients, increased relative plasmid transfer frequencies were observed at ppb (μg/L) levels of tetracycline and sulfamethoxazole micro-contaminant exposure. When sludge, a more complex community, was used as a recipient, the increases of the plasmid transfer rate were always statistically significant but not always in P. aeruginosa. The low concentration (10 ppb) of tetracycline exposure led to the pB10 transfer to enteric bacteria, which are clinically important pathogens.

A coordinate descent MM algorithm for fast computation of sparse logistic PCA

Genome‐Wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single‐nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of Ancestry‐Informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow‐up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from Genome‐Wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a Genome‐Wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of Genome‐Wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a Genome‐Wide study of inflammatory bowel disease. We have implemented our approach in open‐source R software for public use. Genet. Epidemiol. 36:293–302, 2012.

M-type smoothing spline estimators for principal functions

We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a simple bicluster structure and the combination of multiple layers is able to reveal complicated, multiple biclusters. The method allows for non-pure biclusters, and can simultaneously identify the 1-prevalent blocks and 0-prevalent blocks. A computationally efficient algorithm is developed and guidelines are provided for specifying the tuning parameters, including initial values of model parameters, the number of layers, and the penalty parameters. Missing-data imputation can be handled in the EM framework. The method is tested using synthetic and real datasets and shows good performance.

A distribution-free test of constant mean in linear mixed effects models.

Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets.

Canonical correlation analysis for irregularly and sparsely observed functional data

We propose a robust method for estimating principal functions based on MM estimation. Specifically, we formulate functional principal component analysis into alternating penalized M-regression with a bounded loss function. The resulting principal functions are given as M-type smoothing spline estimators. Using the properties of a natural cubic spline, we develop a fast computation algorithm even for long and dense functional data. The proposed method is efficient in that the maximal information from whole observed curve is retained since it partly downweighs abnormally observed individual measurements in a single curve rather than removing or downweighing a whole curve. We demonstrate the performance of the proposed method on simulated and real data and compare it with the conventional functional principal component analysis and other robust functional principal component analysis techniques.

Two sample test for high-dimensional partially paired data

We propose a distribution-free procedure, an analogy of the DIP test in non-parametric regression, to test whether the means of responses are constant over time in repeated measures data. Unlike the existing tests, the proposed procedure requires very minimal assumptions to the distributions of both random effects and errors. We study the asymptotic reference distribution of the test statistic analytically and propose a permutation procedure to approximate the finite-sample reference distribution. The size and power of the proposed test are illustrated and compared with competitors through several simulation studies. We find that it performs well for data of small sizes, regardless of model specification. Finally, we apply our test to a data example to compare the effect of fatigue in two different methods used for cardiopulmonary resuscitation.