Search results for "Resampling"
showing 10 items of 40 documents
Enhancing the retrieval of stream surface temperature from Landsat data
2019
International audience; Thermal images of water bodies often show a radiance gradient perpendicular to the banks. This effect is frequently due to mixed land and water thermal pixels. In the case of the Landsat images, radiance mixing can also affect pure water pixels due the cubic convolution resampling of the native thermal measurements. Some authors recommended a general-purpose margin of two thermal pixels to the banks or a minimum river width of three pixels, to avoid near bank effects in water temperature retrievals. Given the relatively course spatial resolution of satellite thermal sensors, the three pixel margin severely restricts their application to temperature mapping in many ri…
Alder pollen in Finland ripens after a short exposure to warm days in early spring, showing biennial variation in the onset of pollen ripening
2017
Abstract We developed a temperature sum model to predict the daily pollen release of alder, based on pollen data collected with pollen traps at seven locations in Finland over the years 2000–2014. We estimated the model parameters by minimizing the sum of squared errors (SSE) of the model, with weights that put more weight on binary recognition of daily presence or absence of pollen. The model results suggest that alder pollen ripens after a couple of warm days in February, while the whole pollen release period typically takes up to 4 weeks. We tested the model residuals against air humidity, precipitation and wind speed, but adding these meteorological features did not improve the model pr…
Sexing birds using discriminant function analysis: a critical appraisal.
2011
9 pages; International audience; Discriminant function analysis (DFA) based on morphological measurements is a quick, inexpensive, and efficient method for sex determination in field studies on cryptically monomorphic bird species. However, behind the apparent standardization and relative simplicity of DFA lie subtle differences and pitfalls that have been neglected in some studies. Most of these concerns directly affect assessment of the discriminant performance, a parameter of crucial importance in practice because it provides a measure of the quality of an equation that may be used in later field studies. Using results from 141 published studies and simulations based on a large data set …
Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling
2016
Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on …
Stagewise pseudo-value regression for time-varying effects on the cumulative incidence
2015
In a competing risks setting, the cumulative incidence of an event of interest describes the absolute risk for this event as a function of time. For regression analysis, one can either choose to model all competing events by separate cause-specific hazard models or directly model the association between covariates and the cumulative incidence of one of the events. With a suitable link function, direct regression models allow for a straightforward interpretation of covariate effects on the cumulative incidence. In practice, where data can be right-censored, these regression models are implemented using a pseudo-value approach. For a grid of time points, the possibly unobserved binary event s…
Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
2015
The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or model selection is performed on a bootstrap sample as if it were the original sample. P-values computed from bootstrap samples have been used, for example, in the statistics and bioinformatics literature for ranking genes with respect to their differential expression, for estimating the variability of p-v…
Random resampling numerical simulations applied to a SEIR compartmental model
2021
AbstractIn this paper, we apply resampling techniques to a modified compartmental SEIR model which takes into account the existence of undetected infected people in an epidemic. In particular, we implement numerical simulations for the evolution of the first wave of the COVID-19 pandemic in Spain in 2020. We show, by using suitable measures of goodness, that the point estimates obtained by the bootstrap samples improve the ones of the original data. For example, the relative error of detected currently infected people is equal to 0.061 for the initial estimates, while it is reduced to 0.0538 for the mean over all bootstrap estimated series.
The clinical use of statistical permutation test methodology: a tool for identifying predictive variables of outcome.
2015
<b><i>Objectives:</i></b> To identify the predictive variables affecting the outcome after radical surgery for bladder cancer by a newer statistical methodology, i.e. nonparametric combination (NPC). <b><i>Methods:</i></b> A multicenter study enrolled 1,312 patients who had undergone radical cystectomy for bladder cancer in 11 Italian oncological centers from January 1982 to December 2002. A statistical analysis<b> </b>of their medical history and diagnostic, pathological and postoperative variables was performed using a NPC test. The<b> </b>patients were included in a comprehensive database with medical history and cli…
Error estimation and reduction with cross correlations
2010
Besides the well-known effect of autocorrelations in time series of Monte Carlo simulation data resulting from the underlying Markov process, using the same data pool for computing various estimates entails additional cross correlations. This effect, if not properly taken into account, leads to systematically wrong error estimates for combined quantities. Using a straightforward recipe of data analysis employing the jackknife or similar resampling techniques, such problems can be avoided. In addition, a covariance analysis allows for the formulation of optimal estimators with often significantly reduced variance as compared to more conventional averages.
Computation of a few smallest eigenvalues of elliptic operators using fast elliptic solvers
2001
The computation of a few smallest eigenvalues of generalized algebraic eigenvalue problems is studied. The considered problems are obtained by discretizing self-adjoint second-order elliptic partial differential eigenvalue problems in two- or three-dimensional domains. The standard Lanczos algorithm with the complete orthogonalization is used to compute some eigenvalues of the inverted eigenvalue problem. Under suitable assumptions, the number of Lanczos iterations is shown to be independent of the problem size. The arising linear problems are solved using some standard fast elliptic solver. Numerical experiments demonstrate that the inverted problem is much easier to solve with the Lanczos…