A novel stochastic method for reconstructing daily precipitation times-series using tree-ring data from the western Canadian Boreal Forest

Tree ring data provide proxy records of historical hydroclimatic conditions that are widely used for reconstructing precipitation time series. Most previous applications are limited to annual time scales, though information about daily precipitation would enable a range of additional analyses of environmental processes to be investigated and modelled. We used statistical downscaling to simulate stochastic daily precipitation ensembles using dendrochronological data from the western Canadian boreal forest. The simulated precipitation series were generally consistent with observed precipitation data, though reconstructions were poorly constrained during short periods of forest pest outbreaks. The proposed multiple temporal scale precipitation reconstruction can generate annual daily maxima and persistent monthly wet and dry episodes, so that the observed and simulated ensembles have similar precipitation characteristics (i.e. magnitude, peak, and duration)—an improvement on previous modelling studies. We discuss how ecological disturbances may limit reconstructions by inducing non-linear responses in tree growth, and conclude with suggestions of possible applications and further development of downscaling methods for dendrochronological data.     

Under-smoothed kernel confidence intervals for the hazard ratio based on censored data

In cancer clinical trials, it is often of interest in estimating the ratios of hazard rates at some specific time points during the study from two independent populations. In this paper, we consider nonparametric confidence interval procedures for the hazard ratio based on kernel estimates for the hazard rates with under-smoothing bandwidths. Two methods are used to derive the confidence intervals: one based on the asymptotic normality of the ratio of the kernel estimates for the hazard rates in two populations and another through Fieller's Theorem. The performances of the proposed confidence intervals are evaluated through Monte-Carlo simulations and applied to the analysis of data from a clinical trial on early breast cancer.     

A new semiparametric estimation method for accelerated hazard model

The accelerated hazard model has been proposed for more than a decade. However, its application is still very limited, partly due to the complexity of the existing semiparametric estimation method. We propose a new semiparametric estimation method based on a kernel-smoothed approximation to the limit of a profile likelihood function of the model. The method leads to smooth estimating equations and is easy to use. The estimates from the method are proved to be consistent and asymptotically normal. Our numerical study shows that the new method is more efficient than the existing method. The proposed method is employed to reanalyze the data from a brain tumor treatment study.     

Test of marginal compatibility and smoothing methods for exchangeable binary data with unequal cluster sizes

Exchangeable binary data are often collected in developmental toxicity and other studies, and a whole host of parametric distributions for fitting this kind of data have been proposed in the literature. While these distributions can be matched to have the same marginal probability and intra-cluster correlation, they can be quite different in terms of shape and higher-order quantities of interest such as the litter-level risk of having at least one malformed fetus. A sensible alternative is to fit a saturated model (Bowman and George, 1995, Journal of the American Statistical Association 90, 871-879) using the expectation-maximization (EM) algorithm proposed by Stefanescu and Turnbull (2003, Biometrics 59, 18-24). The assumption of compatibility of marginal distributions is often made to link up the distributions for different cluster sizes so that estimation can be based on the combined data. Stefanescu and Turnbull proposed a modified trend test to test this assumption. Their test, however, fails to take into account the variability of an estimated null expectation and as a result leads to inaccurate p-values. This drawback is rectified in this article. When the data are sparse, the probability function estimated using a saturated model can be very jagged and some kind of smoothing is needed. We extend the penalized likelihood method (Simonoff, 1983, Annals of Statistics 11, 208-218) to the present case of unequal cluster sizes and implement the method using an EM-type algorithm. In the presence of covariate, we propose a penalized kernel method that performs smoothing in both the covariate and response space. The proposed methods are illustrated using several data sets and the sampling and robustness properties of the resulting estimators are evaluated by simulations.     

An adaptive bivariate kernel smoothing method for determining instars of Austrosimulium tillyardianum (Diptera: Simuliidae) larvae

1. In insects, instar determination is generally based on the frequency distribution of sclerotised body part measurements. Commonly used univariate methods, such as histograms and univariate kernel smoothing, are not sufficient to reflect the distribution of the measurements, because development of sclerotised body parts is multidimensional. 2. This study used an adaptive bivariate kernel smoothing method, based on 10 pairs of separating variables, to differentiate instars of Austrosimulium tillyardianum (Diptera: Simuliidae) larvae in two‐dimensional space. A variable bandwidth matrix was used and separation lines between instars were defined. Using the Crosby growth ratio, Brooks' rule and the new standard recently proposed, larvae were separated into nine instars. It was found that, using the bivariate kernel smoothing method, the clustering accuracy and determination of separation lines as instar class limits were higher than those associated with the univariate kernel smoothing method. With the exceptions of the paired separating variables, head capsule length and antennal segment 3 length (AS3L), the mean probabilities of correct classifications was > 85%. The pair of separating variables that yielded the greatest classification accuracy comprised mandible length (ML) and AS3L, which had mean probabilities of 0.8984. The clustering accuracy was higher for early‐ and late‐instar larvae, but lower for instars 6 and 7. The adaptive bivariate kernel smoothing method was better than univariate methods for instar determination, especially in the detection of divisions between instars and identification of a larval instar.     

Nonparametric regression using local kernel estimating equations for correlated failure time data

We study nonparametric regression for correlated failure timedata. Kernel estimating equations are used to estimate nonparametriccovariate effects. Independent and weighted-kernel estimatingequations are studied. The derivative of the nonparametric functionis first estimated and the nonparametric function is then estimatedby integrating the derivative estimator. We show that the nonparametrickernel estimator is consistent for any arbitrary working correlationmatrix and that its asymptotic variance is minimized by assumingworking independence. We evaluate the performance of the proposedkernel estimator using simulation studies, and apply the proposedmethod to the western Kenya parasitaemia data.     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

一个适于最大似然法估计物种分化年代的进化速率平滑近似方法

众所周知 ,物种分化年代的估计对分子钟 (进化速率恒定 )假定很敏感。另一方面 ,在远缘物种 (例如哺乳纲不同目的动物 )的比较中 ,分子钟几乎总是不成立的。这样在估计分化时间时考虑不同进化区系的速率差异至为重要。最大似然法可以很自然地考虑这种速率差异 ,并且可以同时分析多个基因位点的资料以及同时利用多重化石校正数据。以前提出的似然法需要研究者将进化树的树枝按速率分组 ,本文提出一个近似方法以使这个过程自动化。本方法综合了以前的似然法、贝斯法及近似速率平滑法的一些特征。此外 ,还对算法加以改进 ,以适应综合数据分析时某些基因在某些物种中缺乏资料的情形。应用新提出的方法来分析马达加斯加的倭狐猴的分化年代 ,并与以前的似然法及贝斯法的分析进行了比较     

A new Sparassis species from Spain described using morphological and molecular data

Sparassis miniensis, collected in Pinus pinaster forests in Galicia (northwest Iberian Peninsula) is described as a new species, based on morphological and molecular data. Sparassis miniensis is morphologically distinct from all other species in the genus Sparassis based on scattered flabellae, which are strongly laciniated, azonate, and arise from an orange to rose-purplish base. The sporadic presence of clamp connections is restricted to subhymenial hyphae. Molecular data from LSU-rDNA, ITS and partial gene coding RNA polymerase subunit II (rpb2) suggest a close relationship between the new species S. miniensis and S. brevipes, another European species producing large fruiting bodies but with entire flabellae and no clamp connections.     

A new method for temperature compensation of electrical conductivity using temperature-fold dependency of fresh water

A new method for temperature compensation of electrical conductivity (EC) is proposed. A relationship between temperature and EC was investigated in detail by a simple experiment using natural and artificial fresh water. Results showed that the temperature dependency of EC varied from 0.03 to 0.02, with temperature increasing from 3°C to 35°C. This means that a traditional method for compensating for EC using a constant coefficient is invalid, so that a temperature-fold dependency must be taken into consideration to standardize EC to a common temperature.     

Study of population dynamic for a recombinant bacterium during continuous cultures: application of data filtering and smoothing

A numerical method to process experimental data concerning plasmid stability of a recombinant bacteria during continuous cultures with nonselective media is proposed here. This method differs from previous ones in that it uses the derivatve form of the state equation of the Imanaka-Aiba model for recombinant cultures. The methodology proposed here allows one to estimate values for the two model parameters without forcing them to be constant. Until now, this could not be done using classical analytical techniques because these parameters have been considered invariable because of the integration used in the evaluation of the model. These parameters are (1) the difference in the specific growth rates between plasmid-carrying cells and plasmid-free cells (deltamu), and (2) the probability of plasmid loss by plasmid-containing cells (rho(r) mu(+)). The derivative technique used here is completed by mathematical treatments involving data filtering and smoothing. The values of the two parameters are in agreement with those already publised. The current technique does not impose preconditions and permit us to further study related phenomena.     

A new method to estimate areas of occupancy using herbarium data

The World Conservation Union (IUCN) Red List of Threatened Species is an important instrument to evaluate the conservation status of living organisms. However, Red List assessors have been limited by the lack of reliable methods to calculate the area of occupancy (AOO) of species, which is an important parameter for red list assessments. Here we present a new practical method to estimate AOO based on herbarium specimen data: the Cartographic method by Conglomerates (CMC). This method, which combines elements from the Areographic and Cartographic methods previously used to calculate AOO, was tested with ten cactus species from the Chihuahuan Desert Region. The results derived from this novel procedure produced in average AOO calculations 3.5 and 5.5 smaller than the Areographic and Cartographic methods, respectively. The CMC takes into account the existence of disjunctions in the distribution range of the species, producing comparatively more accurate AOO estimations. Another advantage of the CMC is that it generates results more harmonic with the current Red List criteria. In contrast, the overestimated results of the Areographic and Cartographic methods tend to artificially categorize the species, even extremely narrow endemics, in lower endangerment status.     

Classifying antibodies using flow cytometry data: class prediction and class discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.     

A method for reconstructing three-dimensional dive profiles of marine mammals using geomagnetic intensity data: results from two lactating Weddell seals

The under-ice behavior of two free-ranging female Weddell seals (Leptonychotes weddellii) was studied using geomagnetic, acceleration and velocity sensors at Big Razorback Island in McMurdo Sound, Antarctica. The seals' body angle and posture were calculated from the acceleration data and the heading from the geomagnetic intensity data. Together with swim speed, the seals' three-dimensional underwater dive path, heading and even posture were reconstructed for each dive. Each instrument was deployed for 2 days, during which time these females made multiple, deep (₞ m) dives, with average maximum depths of 236ᆯ m (n=4) and 244끁 m (n=40). Each seal appeared to choose a particular heading on which to descend. These headings were significantly different between seals and bouts (Watson's U² test, P<0.05). These new instruments and methodologies are shown to provide valuable information on the fine-scale and complex movements of diving animals.     

Regional trends for bud burst and flowering of woody plants in Norway as related to climate change

Data series for bud burst, beginning of flowering and petal fall for 20 species of deciduous trees and conifers at four sites in different regions of southern Norway have been analysed and related to temperature series. On average, the spring phenophases occurred 7 days earlier during the period 1971-2005. The most significant linear trends were observed for the earliest phases. The trends in this period were compared with trends in other periods, the longest one starting in 1927. Those starting in cold decades and ending in 2005 were in most instances statistically significant, whereas hardly any significant trend appeared for series starting in warm decades. This fact showed that the results of trend studies are very sensitive to the choice of starting year. There were significant decadal variations in 40% of the series. The dates of occurrence of the phenophases, varying from the first days of May to the first days of June, correlated with seasonal temperature series, in most cases strongest to mean temperatures for the seasons March-May and April-May. The North Atlantic Oscillation Index (NAOI) for January and February appeared to have some predictive power for the date of occurrence of the recorded phases. The basis for this may be that the oscillations described by the index are of importance for the fulfilment of physiological chilling requirements needed to break bud dormancy. The same genotypes of the trees were grown in region West Norway and in Central Norwegian region; during the period 1965-2005 the trends towards earlier bud burst were more pronounced and steeper at the western site.     

Spatial smoothing and hot spot detection for CGH data using the fused lasso

We apply the "fused lasso" regression method of (TSRZ2004) to the problem of "hot- spot detection", in particular, detection of regions of gain or loss in comparative genomic hybridization (CGH) data. The fused lasso criterion leads to a convex optimization problem, and we provide a fast algorithm for its solution. Estimates of false-discovery rate are also provided. Our studies show that the new method generally outperforms competing methods for calling gains and losses in CGH data.