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Dive into the research topics where Inchi Hu is active.

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Featured researches published by Inchi Hu.


Bioinformatics | 2012

Interaction-based feature selection and classification for high-dimensional biological data

Haitian Wang; Shaw-Hwa Lo; Tian Zheng; Inchi Hu

MOTIVATION Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion. RESULTS We present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by considering interactions. Secondly, a sizable portion of genes identified by our method for breast cancer metastasis overlaps with those reported in gene-to-system breast cancer (G2SBC) database as disease associated and some of them have interesting biological implication. In summary, interaction-based methods may lead to substantial gain in biological insights as well as more accurate prediction.


Genetic Epidemiology | 2011

Inflated Type I Error Rates When Using Aggregation Methods to Analyze Rare Variants in the 1000 Genomes Project Exon Sequencing Data in Unrelated Individuals: Summary Results from Group 7 at Genetic Analysis Workshop 17

Nathan L. Tintle; Hugues Aschard; Inchi Hu; Nora L. Nock; Haitian Wang; Elizabeth W. Pugh

As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype‐phenotype association in next‐generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next‐generation sequencing data: type I error and false‐positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long‐range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes (e.g., nearly 10,000 of 25,000 markers were private variants, and the sample size was small [n = 697]) and nonrandom causes (more correlation was observed than was expected by random chance). Principal components analysis was used to control for population structure and helped to minimize type I errors, but this was at the expense of identifying fewer causal variants. A novel multiple regression approach showed promise to handle correlation between markers. Further work is needed, first, to identify best practices for the control of type I errors in the analysis of sequencing data and then to explore and compare the many promising new aggregating approaches for identifying markers associated with disease phenotypes. Genet. Epidemiol. 35:S56–S60, 2011.


Nucleic Acids Research | 2016

A fast and powerful W-test for pairwise epistasis testing

Maggie Haitian Wang; Rui Sun; Junfeng Guo; Haoyi Weng; Jack Y. B. Lee; Inchi Hu; Pak Sham; Benny Zee

Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a combined log odds ratio. The test is model-free, fast, and inherits a Chi-squared distribution with data adaptive degrees of freedom. No permutation is needed to obtain the P-values. Simulation studies demonstrated that the W-test is more powerful in low frequency variants environment than alternative methods, which are the Chi-squared test, logistic regression and multifactor-dimensionality reduction (MDR). In two independent real bipolar disorder genome-wide associations (GWAS) datasets, the W-test identified significant interactions pairs that can be replicated, including SLIT3-CENPN, SLIT3-TMEM132D, CNTNAP2-NDST4 and CNTCAP2-RTN4R. The genes in the pairs play central roles in neurotransmission and synapse formation. A majority of the identified loci are undiscoverable by main effect and are low frequency variants. The proposed method offers a powerful alternative tool for mapping the genetic puzzle underlying complex disorders.


Computational Statistics & Data Analysis | 2008

Flexible modelling of random effects in linear mixed models-A Bayesian approach

Remus K. W. Ho; Inchi Hu

Flexible modelling of random effects in linear mixed models has attracted some attention recently. In this paper, we propose the use of finite Gaussian mixtures as in Verbeke and Lesaffre [A linear mixed model with heterogeneity in the random-effects population, J. Amu. Statist. Assoc. 91, 217-221]. We adopt a fully Bayesian hierarchical framework that allows simultaneous estimation of the number of mixture components together with other model parameters. The technique employed is the Reversible Jump MCMC algorithm (Richardson and Green [On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion). J. Roy. Statist. Soc. Ser. B 59, 731-792]). This approach has the advantage of producing a direct comparison of different mixture models through posterior probabilities from a single run of the MCMC algorithm. Moreover, the Bayesian setting allows us to integrate over different mixture models to obtain a more robust density estimate of the random effects. We focus on linear mixed models with a random intercept and a random slope. Numerical results on simulated data sets and a real data set are provided to demonstrate the usefulness of the proposed method.


IEEE Transactions on Automatic Control | 1998

On consistency of Bayes estimates in a certainty equivalence adaptive system

Kani Chen; Inchi Hu

The authors analyze a control problem with data generated by the linear regression model where intercept and slope coefficients are unknown. They propose a certainty equivalence control rule based on Bayes estimates of the intercept and slope coefficients. It is shown that the control rule converges to the optimal control rule, which requires complete knowledge of intercept and slope coefficients. Furthermore, under the proposed control rule, if the total control cost tends to infinity, they show that the Bayes estimates for slope and intercept are consistent.


Operations Research | 2011

Efficient Simulation of Value at Risk with Heavy-Tailed Risk Factors

Cheng-Der Fuh; Inchi Hu; Ya-Hui Hsu; Ren-Her Wang

Simulation of small probabilities has important applications in many disciplines. The probabilities considered in value-at-risk (VaR) are moderately small. However, the variance reduction techniques developed in the literature for VaR computation are based on large-deviations methods, which are good for very small probabilities. Modeling heavy-tailed risk factors using multivariate t distributions, we develop a new method for VaR computation. We show that the proposed method minimizes the variance of the importance-sampling estimator exactly, whereas previous methods produce approximations to the exact solution. Thus, the proposed method consistently outperforms existing methods derived from large deviations theory under various settings. The results are confirmed by a simulation study.


Communications in Statistics-theory and Methods | 2003

Empirical Performance and Asset Pricing in Hidden Markov Models

Cheng-Der Fuh; Inchi Hu; Shih-Kuei Lin

Abstract To improve the empirical performance of the Black-Scholes model, many alternative models have been proposed to address leptokurtic feature, volatility smile, and volatility clustering effects of the asset return distributions. However, analytical tractability remains a problem for most alternative models. In this article, we study a class of hidden Markov models including Markov switching models and stochastic volatility models, that can incorporate leptokurtic feature, volatility clustering effects, as well as provide analytical solutions to option pricing. We show that these models can generate long memory phenomena when the transition probabilities depend on the time scale. We also provide an explicit analytic formula for the arbitrage-free price of the European options under these models. The issues of statistical estimation and errors in option pricing are also discussed in the Markov switching models.


Journal of Statistical Planning and Inference | 1998

Strong consistency of Bayes estimates in nonlinear stochastic regression models

Inchi Hu

Abstract A broad range of nonlinear (linear) time series and stochastic processes can be described by the stochastic regression model yn=rn(θ)+en, where {en} are independent random disturbances and rn is a random function of an unknown parameter θ measurable with respect to the σ-field σ(y1,…,yn−1). Here we establish the strong consistency of Bayes estimates in these stochastic regression models under a necessary and sufficient condition on the stochastic regressors, when prior distributions are discrete. This necessary and sufficient condition has been obtained previously by Wu (1981) for nonrandom rn. Herein we extend the result to nonlinear stochastic regression models using results from Shepp (1965) and Shiryayev (1984).


Handbook of Statistical Bioinformatics | 2011

Discovering Influential Variables: A General Computer Intensive Method for Common Genetic Disorders

Tian Zheng; Herman Chernoff; Inchi Hu; Iuliana Ionita-Laza; Shaw-Hwa Lo

We describe a general backward partition method for discovering which of a large number of possible explanatory variables influence a dependent variable Y. This method, based on a variant pioneered by Lo and Zheng, and variations have been used successfully in several biological problems, some of which are discussed here. The problem is an example of feature or variable selection. Although the objective, to understand which are the influential variables, is often not the same as classification, the method has been successfully applied to that problem too.


BMC Proceedings | 2014

A partition-based approach to identify gene-environment interactions in genome wide association studies

Ruixue Fan; Chien-Hsun Huang; Inchi Hu; Haitian Wang; Tian Zheng; Shaw-Hwa Lo

It is believed that almost all common diseases are the consequence of complex interactions between genetic markers and environmental factors. However, few such interactions have been documented to date. Conventional statistical methods for detecting gene and environmental interactions are often based on the linear regression model, which assumes a linear interaction effect. In this study, we propose a nonparametric partition-based approach that is able to capture complex interaction patterns. We apply this method to the real data set of hypertension provided by Genetic Analysis Workshop 18. Compared with the linear regression model, the proposed approach is able to identify many additional variants with significant gene-environmental interaction effects. We further investigate one single-nucleotide polymorphism identified by our method and show that its gene-environmental interaction effect is, indeed, nonlinear. To adjust for the family dependence of phenotypes, we apply different permutation strategies and investigate their effects on the outcomes.

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Cheng-Der Fuh

National Central University

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Maggie Haitian Wang

The Chinese University of Hong Kong

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Benny Zee

The Chinese University of Hong Kong

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Haitian Wang

Hong Kong University of Science and Technology

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Rui Sun

The Chinese University of Hong Kong

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Haoyi Weng

The Chinese University of Hong Kong

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William Ka Kei Wu

The Chinese University of Hong Kong

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