Search results for " resampling"

showing 4 items of 14 documents

Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals

2022

We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor. We concentrate on inference about a single focus parameter, interpreted as the causal effect of a policy or intervention, in the presence of a potentially large number of auxiliary parameters representing the nuisance component of the model. In our Monte Carlo simulations we compare the performance of WALS with that of several competing estimators, including the unrestricted least-squares estimator (with all auxiliary regressors) and the restricted least-squares estimator (with no auxiliary reg…

Shrinkage estimatorStatistics::TheorySettore SECS-P/05Economics Econometrics and Finance (miscellaneous)Linear model WALS condence intervals prediction intervals Monte Carlo simulations.Prediction intervalEstimatorSettore SECS-P/05 - EconometriaComputer Science ApplicationsLasso (statistics)Frequentist inferenceBayesian information criterionStatisticsStatistics::MethodologyAkaike information criterionJackknife resamplingMathematics
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Validation procedures in radiological diagnostic models. Neural network and logistic regression

1999

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validatio…

Validation methodsReceiver operating characteristicArtificial neural networkComputer scienceRadiological weaponResamplingSkull neoplasms logistic regression neural networks receiver operating characteristic curve statistics resamplingStatisticsWord error ratejel:C13Logistic regressionCross-validationjel:C14
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Extrapolation of the Species Accumulation Curve for Incomplete Species Samplings: A New Nonparametric Approach to Estimate the Degree of Sample Compl…

2015

9 pages; International audience; Incomplete species samplings are deemed to remain the common practice in those groups of animals, such as small or micro- invertebrates, with numerous species that often are more or less difficult to detect in the field. Thus, extrapolating the Species Accumulation Curve as far as possible beyond the actual sample size may thus serve as a useful (although imperfect) surrogate to the desired, but practically inaccessible, complete samplings. In this context, several kinds of theoretical or empirical models for the Species Accumulation Curve and also a lot of estimators of the asymptotic limit of the Curve (i.e. total species richness) have been proposed. The …

[STAT.AP]Statistics [stat]/Applications [stat.AP][ SDE.BE ] Environmental Sciences/Biodiversity and Ecologyspecies accumulation curveSpecies discovery curve[ STAT.AP ] Statistics [stat]/Applications [stat.AP]ExtrapolationNonparametric statisticsExtrapolationChao estimatorSampling (statistics)Sample (statistics)General Biochemistry Genetics and Molecular Biologystop samplingjackknifeStatisticsnon-parametricPoint estimationSpecies richness[SDE.BE]Environmental Sciences/Biodiversity and Ecologypoint estimationspecies richnessGeneral Agricultural and Biological SciencesJackknife resamplingsample completenessMathematicsAnnual Research & Review in Biology
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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

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 prediction …

pollen seasonMonte Carlo -menetelmätlepätkukintaotorhinolaryngologic diseasesfood and beveragesmodelingMonte Carlo resamplingennusteetAlnusleppäsiitepöly
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