An estimating function approach to inference for inhomogeneous Neyman-Scott processes

This article is concerned with inference for a certain class of inhomogeneous Neyman-Scott point processes depending on spatial covariates. Regression parameter estimates obtained from a simple estimating function are shown to be asymptotically normal when the "mother" intensity for the Neyman-Scott process tends to infinity. Clustering parameter estimates are obtained using minimum contrast estimation based on the K-function. The approach is motivated and illustrated by applications to point pattern data from a tropical rain forest plot.     

生态环境承载力评价研究——以河南省为例

测度人类活动是否处于生态环境的承载力范围之内是生态环境承载力评价的核心内容,是区域国土开发科学决策规划的需要,但是生态环境可承载能力的阈值和人类活动对生态环境施加压力的大小均是难以定量计算的,为了解决生态环境承载力评价面临的这一难题,本文研究提出了一种生态环境承载力评价的新方法,即"生态环境质量指数动态评价法"。该方法依据生态平衡理论和生态稳定性原理,将承载力评价转化为运行状态评价,以"生态环境系统是否处于良性运行状态"作为生态环境是否处于可承载状态的评价标准;首先应用生态环境质量指数(EI)模型计算出评价区多年的EI值,然后再依据EI的动态变化趋势对"生态环境系统的运行状态"进行评定,以达到对区域生态环境承载力进行评价之目的,以河南省为例,介绍了评价的方法与过程。结果表明:(1)生态环境质量现状评价不是承载力评价,但是可通过对生态环境质量的动态变化趋势进行回归分析以达到承载力评价之目的;(2)该方法紧扣生态环境承载力的内涵,避开了生态环境系统承载力阈值测算的难点,方法简明易行,结果意义明确、实用性强,为承载力评价提供了一种新思路和新方法;(3)生态环境系统质量现状不能反映其承载状态,信阳市的生态环境质量在河南是最好的,目前却处于超载、退化状态,而濮阳和安阳市的情况则与信阳市正好相反,应引起政府决策部门的重视。     

Faecal egg counts provide a reliable measure of Trichostrongylus tenuis intensities in free-living red grouse Lagopus lagopus scoticus

The reliability of different egg counting methods for estimating the intensity of Trichostrongylus tenuis infections in red grouse, Lagopus lagopus scoticus, was investigated in the autumn, when grouse may harbour high parasite intensities. Possible limitations to the use of these methods were also examined. Faecal egg counts were found to accurately estimate T. tenuis worm intensities, at least up to an observed maximum of c. 8000 worms. Two egg counting methods (smear and McMaster) gave consistent results, although the exact relationship with worm intensity differed according to the method used. Faecal egg counts significantly decreased with increasing length of sample storage time, but egg counts were reliable for estimating worm intensity for three weeks. The concentration of eggs in the caecum was also found to reliably estimate worm intensity. However, egg counts from frozen gut samples cannot be used to estimate worm intensities. These results conclude that, despite some limitations, faecal and caecum egg counts provide useful and reliable ways of measuring T. tenuis intensities in red grouse.     

Modelling the effects of partially observed covariates on Poisson process intensity

We propose an estimating function for parameters in a modelfor Poisson process intensity when time- or space-varying covariatesare observed for both the events of the process and at sampletimes or locations selected from a probability-based samplingdesign. We investigate the large-sample properties of the proposedestimator under increasing domain asymptotics, demonstratingthat it is consistent and asymptotically normally distributed.We illustrate our approach using data from an ecological momentaryassessment of smoking.     

Estimating functions for inhomogeneous spatial point processes with incomplete covariate data

The R package spatstat provides a very flexible and useful frameworkfor analysing spatial point patterns. A fundamental featureis a procedure for fitting spatial point process models dependingon covariates. However, in practice one often faces incompleteobservation of the covariates and this leads to parameter estimationerror which is difficult to quantify. In this paper, we introducea Monte Carlo version of the estimating function used in spatstatfor fitting inhomogeneous Poisson processes and certain inhomogeneouscluster processes. For this modified estimating function, itis feasible to obtain the asymptotic distribution of the parameterestimators in the case of incomplete covariate information.This allows a study of the loss of efficiency due to the missingcovariate data.     

Bayesian deconvolution analysis of pulsatile hormone concentration profiles

Many hormones are secreted into the circulatory system in a pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into a secretion function and a clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date, deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present a novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as a point process. Pulses are determined by a birth-death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics 28, 40-74) in the context of finite mixture model density estimation, where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes.     

The validity of burrow counting in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat at Yangtze Estuary

It has been confirmed that the crabs play significant roles in the structure and function of coastal wetland ecosystems, such as mangroves and salt marshes. However it is not easy to estimate the abundance and density of burrowing crabs effectively, thus a further understanding of roles of crabs in these ecosystems has been lagged. Some studies have discussed the suitability of several census techniques, such as burrow counting method in estimating crab density in mangroves. The validities of burrow counting method and other census techniques in estimating crab density, however, has not been tested in salt marshes, especially where vegetation are dense. In this study, we tested the validity of burrow counting method in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat with dense vegetation of Phragmites australis and Zizania aquatica at Yangtze Estuary through comparing densities estimated by the burrow counting method and the excavation. Burrow counting averagely underestimated the density of C. dehaani by 15% and the degree of underestimation varied among vegetations and habitats (from an overestimate by 23% to underestimate by 41%). Burrow counting averagely overestimated the density of I. deschampsi by 43% and the degree of overestimate varied from 0% to 133% depending on the vegetations and habitats. The percentage of occupied burrows and the number of crabs sharing one burrow were important factors influencing the validity of estimating crab density through burrow counting method.     

The validity of burrow counting in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat at Yangtze Estuary

It has been confirmed that the crabs play significant roles in the structure and function of coastal wetland ecosystems, such as mangroves and salt marshes. However it is not easy to estimate the abundance and density of burrowing crabs effectively, thus a further understanding of roles of crabs in these ecosystems has been lagged. Some studies have discussed the suitability of several census techniques, such as burrow counting method in estimating crab density in mangroves. The validities of burrow counting method and other census techniques in estimating crab density, however, has not been tested in salt marshes, especially where vegetation are dense. In this study, we tested the validity of burrow counting method in estimating the densities of Chiromantes dehaani and Ilyoplax deschampsi in tidal flat with dense vegetation of Phragmites australis and Zizania aquatica at Yangtze Estuary through comparing densities estimated by the burrow counting method and the excavation. Burrow counting averagely underestimated the density of C. dehaani by 15% and the degree of underestimation varied among vegetations and habitats (from an overestimate by 23% to underestimate by 41%). Burrow counting averagely overestimated the density of I. deschampsi by 43% and the degree of overestimate varied from 0% to 133% depending on the vegetations and habitats. The percentage of occupied burrows and the number of crabs sharing one burrow were important factors influencing the validity of estimating crab density through burrow counting method.     

A Note on Variance Estimation of the Aalen–Johansen Estimator of the Cumulative Incidence Function in Competing Risks,with a View towards Left‐Truncated Data

The Aalen–Johansen estimator is the standard nonparametric estimator of the cumulative incidence function in competing risks. Estimating its variance in small samples has attracted some interest recently, together with a critique of the usual martingale‐based estimators. We show that the preferred estimator equals a Greenwood‐type estimator that has been derived as a recursion formula using counting processes and martingales in a more general multistate framework. We also extend previous simulation studies on estimating the variance of the Aalen–Johansen estimator in small samples to left‐truncated observation schemes, which may conveniently be handled within the counting processes framework. This investigation is motivated by a real data example on spontaneous abortion in pregnancies exposed to coumarin derivatives, where both competing risks and left‐truncation have recently been shown to be crucial methodological issues (Meister and Schaefer (2008), Reproductive Toxicology 26 , 31–35). Multistate‐type software and data are available online to perform the analyses. The Greenwood‐type estimator is recommended for use in practice.     

Spline-based semiparametric projected generalized estimating equation method for panel count data

We propose to analyze panel count data using a spline-based semiparametric projected generalized estimating equation (GEE) method with the proportional mean model E(N(t)|Z) = Λ(0)(t) e(β(0)(T)Z). The natural logarithm of the baseline mean function, logΛ(0)(t), is approximated by a monotone cubic B-spline function. The estimates of regression parameters and spline coefficients are obtained by projecting the GEE estimates into the feasible domain using a weighted isotonic regression (IR). The proposed method avoids assuming any parametric structure of the baseline mean function or any stochastic model for the underlying counting process. Selection of the working covariance matrix that accounts for overdispersion improves the estimation efficiency and leads to less biased variance estimations. Simulation studies are conducted using different working covariance matrices in the GEE to investigate finite sample performance of the proposed method, to compare the estimation efficiency, and to explore the performance of different variance estimates in presence of overdispersion. Finally, the proposed method is applied to a real data set from a bladder tumor clinical trial.     

Standardized protocols for characterizing women's fertility: A data-driven approach

Experts are divided on whether women's cognition and behavior differs between fertile and non-fertile phases of the menstrual cycle. One of the biggest criticisms of this literature concerns the use of indirect, imprecise, and flexible methodologies between studies to characterize women's fertility. To resolve this problem, we provide a data-driven method of best practices for characterizing women's fertile phase. We compared the accuracy of self-reported methods and counting procedures (i.e., the forward- and backward-counting methods) in estimating ovulation using data from 140 women whose fertility was verified with luteinizing hormone tests. Results revealed that no counting method was associated with ovulation with > 30% accuracy. A minimum of 39.5% of the days in the six-day fertile window predicted by the counting methods were non-fertile, and correlations between counting method conception probabilities and actual conception probability were weak to moderate, rs = 0.11–0.30. Poor results persisted when using a lenient window for predicting ovulation, across alternative estimators of the onset of the next cycle, and when removing outliers to increase the homogeneity of the sample. By contrast, combining counting methods with a relatively inexpensive test of luteinizing hormone predicted fertility with accuracy > 95%, but only when specific guidelines were followed. To this end, herein we provide a cost-effective, pragmatic, and standardized protocol that will allow researchers to test whether fertility effects exist or not.     

Bayesian Inference for a Random Tessellation Process

This article describes an inhomogeneous Poisson point process in the plane with an intensity function based on a Dirichlet tessellation process and a method for using observations on the point process to make fully Bayesian inferences about the underlying tessellation. The method is implemented using a Markov chain Monte Carlo approach. An application to modeling the territories of clans of badgers, Meles meles, is described.     

Problem in liquid scintillation counting: adsorption of (14-C)pholyethylene glycol in dilute solutions.

[¹⁴C]polyethylene glycol is the method of choice for quantitating changes in intestinal water flux during drug absorption experiments in animals and man. This study points out some of the problems which can be encountered in using this method and provides ways to minimize these problems. Polyethylene glycol selectively binds to the glass wall of scintillation vials during counting and results in a decrease in counting efficiency as a function of time. The results obtained when using this method are determined by the choice of scintillation vial, scintillation cocktail, concentration of polyethylene glycol and the time period over which the samples are counted.     

基于近红外光谱实时监测乙醇发酵过程多组分参数

生物量、葡萄糖浓度和乙醇浓度是乙醇发酵过程的重要参数,传统的方法通常对发酵液取样作离线测量,不仅需要采用多种仪器进行测试分析,而且耗时耗力,成为实时过程调控和优化的障碍。文中针对这些重要过程参数提出了一个基于近红外光谱技术的原位实时检测方法。通过采用浸入式近红外光谱仪对发酵溶液进行原位测量,基于多输出最小二乘支持向量机回归(MLS-SVR)方法建立了利用近红外光谱同时分析葡萄糖浓度、生物量和乙醇浓度的多输出预测模型。实验结果表明,该方法能实时准确地检测乙醇发酵过程中的葡萄糖浓度、生物量和乙醇浓度,而且相对于现有的偏最小二乘法(PLS)分别对各组分建模和预测,能明显提高测量准确性和可靠性。     

Modeling nonhomogeneous Markov processes via time transformation

Summary .   Longitudinal studies are a powerful tool for characterizing the course of chronic disease. These studies are usually carried out with subjects observed at periodic visits giving rise to panel data. Under this observation scheme the exact times of disease state transitions and sequence of disease states visited are unknown and Markov process models are often used to describe disease progression. Most applications of Markov process models rely on the assumption of time homogeneity, that is, that the transition rates are constant over time. This assumption is not satisfied when transition rates depend on time from the process origin. However, limited statistical tools are available for dealing with nonhomogeneity. We propose models in which the time scale of a nonhomogeneous Markov process is transformed to an operational time scale on which the process is homogeneous. We develop a method for jointly estimating the time transformation and the transition intensity matrix for the time transformed homogeneous process. We assess maximum likelihood estimation using the Fisher scoring algorithm via simulation studies and compare performance of our method to homogeneous and piecewise homogeneous models. We apply our methodology to a study of delirium progression in a cohort of stem cell transplantation recipients and show that our method identifies temporal trends in delirium incidence and recovery.     

Bayesian nonparametric inference for panel count data with an informative observation process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects, and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.     

De novo peptide sequencing using ion peak intensity and amino acid cleavage intensity ratio

MOTIVATION: Peptide-sequencing methods by mass spectrum use the following two approaches: database searching and de novo sequencing. The database-searching approach is convenient; however, in cases wherein the corresponding sequences are not included in the databases, the exact identification is difficult. On the other hand, in the case of de novo sequencing, no preliminary information is necessary; however, continuous amino acid sequence peaks and the differentiation of these peaks are required. It is, however, very difficult to obtain and differentiate the peaks of all amino acids by using an actual spectrum. We propose a novel de novo sequencing approach using not only mass-to-charge ratio but also ion peak intensity and amino acid cleavage intensity ratio (CIR). RESULTS: Our method compensates for any undetectable amino acid peak intervals by estimating the amino acid set and the probability of peak expression based on amino acid CIR. It provides more accurate identification of sequences than the existing methods, by which it is usually difficult to sequence.     

Estimation of Spatiotemporal Neural Activity Using Radial Basis Function Networks

We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.     

Analysis of Time-Varying Biological Data Using Rainflow Cycle Counting

A wide range of biological investigations lead to time-history data. The characterization of such data can be difficult particularly in the presence of signal noise or superimposed signals. Several methods are described which can be brought to bear including FFT, thresholding, peak counting, and range counting. However, each of these approaches has significant disadvantages. In this paper we describe a novel method, known as rainflow cycle counting, for characterizing time varying biological time-history data in terms of spiking or oscillation amplitude and frequency. Rainflow counting is a straightforward algorithm for identifying complete cycles in the data and determining their amplitudes. The approach is simple, reliable, easily lends itself to automation, and robust even in the presence of superimposed signals or background noise. After describing the method, its use and behavior are demonstrated on three sample histories of intracellular calcium concentration in chondrocytes exposed to fluid shear stress. The method is also applied to a more challenging data set that has had an artificial random error included. The results demonstrate that the rainflow counting algorithm identifies signal oscillations and appropriately determines their amplitudes even when superimposed or distorted by background noise. These attractive properties make rainflow counting a powerful approach for quantifying and characterizing biological time histories.