From the help desk: Bootstrapped standard errors

Bootstrapping is a nonparametric approach for evaluating the distribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software. Copyright 2003 by Stata Corporation.st0034, bootstrap, cluster, nl, instrumental variables

Models and Methods for Genome-Wide Association Studies.

Genome-wide association (GWA) studies provide an extensive assessment of common genetic variants across the human genome for disease association. However, due to variation in allele frequencies and disease prevalence across populations, combining samples from different geographic or ethnic groups may lead to spurious evidence for association or diminish the true association signals. In part one of this dissertation, I propose a novel approach to correct for population stratification that makes use of the large amount of genetic information available in a GWA study. Based on allele-sharing identity-by-state (IBS) measures, I develop similarity scores that can describe genetic similarity between individuals, and match cases and controls accordingly. Association tests can then be performed conditional on the matched case-control groups. I apply our approach to the Pritzker bipolar GWA study. In part two, I extend our matching approach to families of arbitrary structure. I first apply similarity score-based matching to selected members from each family and then assign other family members to the same matched group. I modify a corrected chi-square test [Bourgain et al., 2003] following the Mantel-Haenszel procedure to account for correlations both between the family samples and between the matched cases and controls. The rapid advance in next-generation sequencing technologies allows a near-complete survey of genomic regions of interest and even whole genomes, enabling more extensive genetic association studies of rare variants. As we plan such re-sequencing studies of a complex disease, it is useful to consider the range of plausible genetic models, e.g., risk allele frequency (RAF) and genotype relative risk (GRR) of rare or less common causal variants, based on results of previous genetic linkage and association studies for the trait. In part three, I compute the power to detect linkage and/or association as a function of genetic model, and summarize the range of models likely to yield results that are consistent with existing GWA and/or linkage studies.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77921/1/wguan_1.pd

AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization

The rapid growth of information on the Internet has led to an overwhelming amount of opinions and comments on various activities, products, and services. This makes it difficult and time-consuming for users to process all the available information when making decisions. Text summarization, a Natural Language Processing (NLP) task, has been widely explored to help users quickly retrieve relevant information by generating short and salient content from long or multiple documents. Recent advances in pre-trained language models, such as ChatGPT, have demonstrated the potential of Large Language Models (LLMs) in text generation. However, LLMs require massive amounts of data and resources and are challenging to implement as offline applications. Furthermore, existing text summarization approaches often lack the ``adaptive" nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific requirements or preferences. In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization. The model generates aspect-oriented summaries given a set of reviews for a particular product, efficiently providing users with useful information on specific aspects they are interested in, ensuring the generated summaries are more personalized and informative. Extensive experiments have been conducted using real-world datasets to evaluate the proposed model. The results demonstrate that our model outperforms state-of-the-art approaches and is adaptive and efficient in generating summaries that focus on particular aspects, enabling users to make well-informed decisions and catering to their diverse interests and preferences.Comment: 21 pages, 4 figures, 7 table