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Department of Mathematics, University of Idaho We compare convergence rates of Metropolis–Hastings chains to multi-modal target distributions when the proposal distributions can be of “local” and “small world” type. In particular, we show that by adding occasional long-range jumps to a given local proposal distribution, one can turn a chain that is “slowly… (More)

- Yongtao Guan, David R. Afshartous
- Environmental and Ecological Statistics
- 2007

Ecological data often involve measurements taken at irregularly spaced locations (e.g., the heights of trees in a forest). A useful approach for modeling such data is via a marked point process, where the marks (i.e., measurements) and points (i.e., locations) are often assumed to be independent. Although this is a convenient assumption, it may not hold in… (More)

This paper is concerned with parameter estimation for inhomogeneous spatial point processes with a regression model for the intensity function and tractable second order properties (K-function). Regression parameters are estimated using a Poisson likelihood score estimating function and in a second step minimum contrast estimation is applied for the… (More)

- Yongtao Guan, Roland Fleißner, Paul Joyce, Stephen M. Krone
- Statistics and Computing
- 2006

As the number of applications for Markov Chain Monte Carlo (MCMC) grows, the power of these methods as well as their shortcomings become more apparent. While MCMC yields an almost automatic way to sample a space according to some distribution, its implementations often fall short of this task as they may lead to chains which converge too slowly or get… (More)

- Yongtao Guan
- 2007

The pair correlation function is a useful tool to analyze spatial point patterns. It is often estimated nonparametrically by a procedure such as kernel smoothing. This article develops a data-driven method for the selection of the bandwidth involved in the estimation. The proposed method uses the idea of least-squares cross-validation which has been often… (More)

- YONGTAO GUAN
- 2007

We introduce two new variance estimation procedures by using non-overlapping and overlapping blocks, respectively. The non-overlapping block (NOB) estimator can be viewed as the limit of the thinned block bootstrap (TBB) estimator recently proposed in Guan and Loh (2007), by letting the number of thinned processes and bootstrap samples therein both increase… (More)

- Stephen M Krone, Yongtao Guan
- Journal of theoretical biology
- 2006

Biological communities are remarkable in their ability to form cooperative ensembles that lead to coexistence through various types of niche partitioning, usually intimately tied to spatial structure. This is especially true in microbial settings where differential expression and regulation of genes allows members of a given species to alter their lifestyle… (More)

- Rong Wang, Cary P Gross, +7 authors Xiaomei Ma
- Leukemia research
- 2012

During 2004-2006, two hypomethylating agents (HMAs) were approved for the treatment of myelodysplastic syndromes (MDS) in the United States. We assessed the impact of HMAs on the cost of care and survival of MDS patients, by constructing a cohort of patients who were diagnosed during 2001-2007 (n=6556, age ≥66.5 years) and comparable non-cancer controls. We… (More)

- Abdollah Jalilian, Yongtao Guan, Rasmus Waagepetersen
- Scandinavian journal of statistics, theory and…
- 2013

Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover… (More)

- Yongtao Guan, Michael Sherman, James A Calvin
- Biometrics
- 2006

A common assumption while analyzing spatial point processes is direction invariance, i.e., isotropy. In this article, we propose a formal nonparametric approach to test for isotropy based on the asymptotic joint normality of the sample second-order intensity function. We derive an L(2) consistent subsampling estimator for the asymptotic covariance matrix of… (More)