Lan Xue

Oregon State University | OSU · Department of Statistics

We consider the estimation of the boundary of a set when it is known to be sufficiently smooth, to satisfy certain shape constraints and to have an additive structure. Our proposed method is based on spline estimation of a conditional quantile regression and is resistant to outliers and/or extreme values in the data. This work is a desirable extens...

The spotted-wing drosophila, Drosophila suzukii Matsumura, is an invasive pest causing significant damage to soft skinned fruits. Control of D. suzukii is critical since there is no tolerance for infested fruit in the market. While most insecticides control one or more D. suzukii life-stages (e.g., egg, larvae, and adult), the impact of insecticide...

Production frontier is an important concept in modern economics and has been widely used to measure production efficiency. Existing nonparametric frontier models often only allow one or low-dimensional input variables due to ‘curse-of-dimensionality’. In this paper we propose a flexible additive frontier model which quantifies the effects of multip...

We propose time‐varying coefficient model selection and estimation based on the spline approach, which is capable of capturing time‐dependent covariate effects. The new penalty function utilizes local‐region information for varying‐coefficient estimation, in contrast to the traditional model selection approach focusing on the entire region. The pro...

Monotone additive models are useful in estimating productivity curve or analyzing disease risk where the predictors are known to have monotonic effects on the response. Existing literature mainly focuses on univariate monotone smoothing. Available methods for estimation of monotone additive models are either difficult to interpret or have no asympt...

The zebrafish's potential as a model for human neurobehavioral research appears nearly limitless despite its relatively recent emergence as an experimental organism. Since the zebrafish has only been part of the research community for a handful of decades, pathogens from its commercial origins continue to plague laboratory stocks. One such pathogen...

Pseudoloma neurophilia is a microsporidium of zebrafish (Danio rerio) that preferentially infects neural tissue. It is one of the most common pathogens of zebrafish in research laboratories based on diagnostic data from the Zebrafish International Resource Center diagnostic service (Eugene, OR). Five hundred fifty-nine zebrafish infected with P. ne...

Manayunkia speciosa is the obligate invertebrate host of Ceratonova (syn Ceratomyxa) shasta (Myxozoa), the parasite that causes ceratomyxosis (enteronecrosis) in salmon and trout. High peak discharge has been correlated with reduced ceratomyxosis in salmon hosts but how it may influence parasite dynamics in the invertebrate host is unknown. We samp...

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of t...

We studied stochastic additive models (SAM) for nonlinear time series data. We proposed a penalised polynomial spline (PPS) method for estimation and lag selection in SAM. This method approximated the nonparametric functions by polynomial splines and performed variable/lag selection by imposing a penalty on the empirical L 2 norm of the spline func...

The varying-coefficient model is flexible and powerful for modeling the dynamic changes of regression coefficients. It is important to identify significant covariates associated with response variables, especially for high-dimensional settings where the number of covariates can be larger than the sample size. We consider model selection in the high...

Background/Question/Methods The parasite, Ceratomyxa shasta, may be strongly influenced by population dynamics of its invertebrate host, the polychaete, Manayunkia speciosa. The polychaete host directly influences parasite transmission to salmonids but our understanding of factors that influence parasite transmission is hampered by a lack of under...

Dams along the Deschutes River (DR) in central Oregon have blocked fish migration for over 40 years. Reestablishment of anadromous fish runs above the dams as part of a fish passage plan may introduce fish pathogens, such as Myxobolus cerebralis, the myxozoan parasite that causes salmonid whirling disease. This parasite is carried by adult salmon t...

We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified l...

A smooth monotone polynomial spline (PS) estimator is proposed for the cumulative distribution function. The proposed method applies a constrained PS regression to smooth the empirical distribution function, while simultaneously ensures monotonicity by imposing a set of linear constraints on the coefficients of the PS functions. This feature is not...

Tubifex tubifex are obligate invertebrate hosts in the life cycle of Myxobolus cerebralis, the myxozoan parasite that causes whirling disease in salmonid fishes. This exotic parasite is established to varying degrees across Oregon’s Columbia River system (Pacific Northwest, USA) and characteristics of local T. tubifex populations likely play a role...

We propose a new estimation method for multivariate failure time data using the quadratic inference function (QIF) approach. The proposed method efficiently incorporates within-cluster correlations. Therefore, it is more efficient than those that ignore within-cluster correlation. Furthermore, the proposed method is easy to implement. Unlike the we...

We propose a penalized polynomial spline method for simultaneous model estimation and variable selection in additive models. It approximates nonparamet-ric functions by polynomial splines, and minimizes the sum of squared errors subject to an additive penalty on norms of spline functions. This approach sets estimators of certain function components...

We study a semiparametric generalized additive coefficient model, in which linear predictors in the conventional generalized linear models is generalized to unknown functions depending on certain covariates, and approximate the nonparametric functions by using polynomial spline. The asymptotic expansion with optimal rates of convergence for the est...

A flexible nonparametric regression model is considered in which the re-sponse depends linearly on some covariates, with regression coefficients as additive functions of other covariates. Polynomial spline estimators are proposed for the unknown coefficient functions, with optimal univariate mean square convergence rate under geometric mixing condi...

We propose marginal integration estimation and testing methods for coefficients of varying-coefficient multivariate regression models. Asymptotic distribution theory is developed for the estimation method, which enjoys the same rate of convergence as univariate function estimation. For the test statistic, asymptotic normal theory is established. Th...

In the multivariate regression setting, we propose a flexible varying coefficient model in which the regression coefficients of some predictors are additive functions of other predictors. Marginal integration estimators of the coefficients are developed and their asymptotic properties investigated. Under β-mixing, it is found that the estimators of...

Thesis (Ph. D.)--Michigan State University. Dept. of Statistics and Probability, 2005. Includes bibliographical references (leaves 112-114).