Search

Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly non-linear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a prediction frequency-based ensemble that identifies core prediction values, which are core prediction members to be used in the ensemble and are expected to be concentrated near the true response. The prediction frequency-based ensemble classifies core prediction values supported by multiple NN models by conducting statistical analysis with a frequency distribution, which is a collection of prediction values obtained from various NN models for a given prediction point. The prediction frequency-based ensemble searches for a range of prediction values that contains prediction values above a certain frequency, and thus the predictive performance can be improved by excluding prediction values with low accuracy and coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the prediction frequency-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the prediction frequencybased ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the prediction frequency-based ensemble compared with the previously developed space-filling and prediction variance-based strategies. [ABSTRACT FROM AUTHOR]

One way to improve the value of citizen science data for a specific aim is through promoting adaptive sampling, where the marginal value of a citizen science observation is dependent on existing data collected to address a specific question. Adaptive sampling could increase sampling at places or times—using a dynamic and updateable framework—where data are expected to be most informative for a given ecological question or conservation goal. We used an experimental approach to test whether the participants in a popular Australian citizen science project—FrogID—would follow an adaptive sampling protocol aiming to maximize understanding of frog diversity. After a year, our results demonstrated that these citizen science participants were willing to adopt an adaptive sampling protocol, improving the sampling of biodiversity consistent with a specific aim. Such adaptive sampling can increase the value of citizen science data for biodiversity research and open up new avenues for citizen science project design. [ABSTRACT FROM AUTHOR]

We present improved sampling complexity bounds for stable and robust sparse recovery in compressed sensing. Our unified analysis based on |$\ell ^1$| minimization encompasses the case where (i) the measurements are block-structured samples in order to reflect the structured acquisition that is often encountered in applications and (ii) the signal has an arbitrary structured sparsity, by results depending on its support |$S$|⁠. Within this framework and under a random sign assumption, the number of measurements needed by |$\ell ^1$| minimization can be shown to be of the same order than the one required by an oracle least-squares estimator. Moreover, these bounds can be minimized by adapting the variable density sampling to a given prior on the signal support and to the coherence of the measurements. We illustrate both numerically and analytically that our results can be successfully applied to recover Haar wavelet coefficients that are sparse in levels from random Fourier measurements in dimension one and two, which can be of particular interest in imaging problems. Finally, a preliminary numerical investigation shows the potential of this theory for devising adaptive sampling strategies in sparse polynomial approximation. [ABSTRACT FROM AUTHOR]

The reverse time migration of an elastic wave is an advanced method for an imaging velocity model, and the reverse time migration of an elastic inhomogeneous TTI medium has become a research hotspot. The amount of calculation and storage is an important factor restricting its development. To solve the storage problem, we improve the wave field separation algorithm using adaptive sampling in the process of reverse time migration. This resampling algorithm can retain the core region and resample the marginal area of a quasi-differential operator. After sampling we only need to save the sampling point and the sampling location. It is proved by model calculation that the storage capacity of the algorithm can reduce a large part of the computational time of a traditional algorithm without reducing calculation accuracy. The new algorithm is more suitable for GPU because the storage is greatly reduced. In the process of reverse time migration, we can store the quasi-differential operator in the GPU so as to avoid a lot of I/O of GPU to CPU and improve calculation efficiency. [ABSTRACT FROM AUTHOR]

Modeling correlated or highly stratified multiple‐response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time‐consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active‐learning methods found in the machine‐learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method. [ABSTRACT FROM AUTHOR]

The article presents the author's response to comments made regarding his article "On the External Validity of Experiments in Consumer Research." The commentators disputed the author's argument that theoretical researchers should concern themselves with the generalizability of their research findings. The author contends that the commentators failed to distinguish the research methods which are commonly thought to increase external validity from the concept of external validity. He concludes that theoretical researchers should use procedures such as the "selective approach" and the "model of deliberate sampling for heterogeneity."

This paper deals with the design of the likelihood continual reassessment method, which is an increasingly widely used model-based method for dose-finding studies. It is common to implement the method in a two-stage approach, whereby the model-based stage is activated after an initial sequence of patients has been treated. While this two-stage approach is practically appealing, it lacks a theoretical framework, and it is often unclear how the design components should be specified. This paper develops a general framework based on the coherence principle, from which we derive a design calibration process. A real clinical-trial example is used to demonstrate that the proposed process can be implemented in a timely and reproducible manner, while offering competitive operating characteristics. We explore the operating characteristics of different models within this framework and show the performance to be insensitive to the choice of dose-toxicity model. [ABSTRACT FROM PUBLISHER]

A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population. [ABSTRACT FROM AUTHOR]

Our paper proposes adaptive Monte Carlo sampling schemes for Bayesian variable selection in linear regression that improve on standard Markov chain methods. We do so by considering Metropolis–Hastings proposals that make use of accumulated information about the posterior distribution obtained during sampling. Adaptation needs to be done carefully to ensure that sampling is from the correct ergodic distribution. We give conditions for the validity of an adaptive sampling scheme in this problem, and for simulating from a distribution on a finite state space in general, and suggest a class of adaptive proposal densities which uses best linear prediction to approximate the Gibbs sampler. Our sampling scheme is computationally much faster per iteration than the Gibbs sampler, and when this is taken into account the efficiency gains when using our sampling scheme compared to alternative approaches are substantial in terms of precision of estimation of posterior quantities of interest for a given amount of computation time. We compare our method with other sampling schemes for examples involving both real and simulated data. The methodology developed in the paper can be extended to variable selection in more general problems. [ABSTRACT FROM AUTHOR]

The usual design-unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics—not necessarily minimal—are described. First, estimators that are as easy to compute as the usual design-unbiased estimators are given. Estimators obtained by conditioning on the minimal sufficient statistic which are more difficult to compute are also discussed. Estimators are compared in examples. [ABSTRACT FROM AUTHOR]

Deconvolution problems are naturally represented in the Fourier domain, whereas thresholding in wavelet bases is known to have broad adaptivity properties. We study a method which combines both fast Fourier and fast wavelet transforms and can recover a blurred function observed in white noise with O{ n log ( n)2} steps. In the periodic setting, the method applies to most deconvolution problems, including certain ‘boxcar’ kernels, which are important as a model of motion blur, but having poor Fourier characteristics. Asymptotic theory informs the choice of tuning parameters and yields adaptivity properties for the method over a wide class of measures of error and classes of function. The method is tested on simulated light detection and ranging data suggested by underwater remote sensing. Both visual and numerical results show an improvement over competing approaches. Finally, the theory behind our estimation paradigm gives a complete characterization of the ‘maxiset’ of the method: the set of functions where the method attains a near optimal rate of convergence for a variety of L p loss functions. [ABSTRACT FROM AUTHOR]

The use of a fixed rejection region for multiple hypothesis testing has been shown to outperform standard fixed error rate approaches when applied to control of the false discovery rate. In this work it is demonstrated that, if the original step-up procedure of Benjamini and Hochberg is modified to exercise adaptive control of the false discovery rate, its performance is virtually identical to that of the fixed rejection region approach. In addition, the dependence of both methods on the proportion of true null hypotheses is explored, with a focus on the difficulties that are involved in the estimation of this quantity. [ABSTRACT FROM AUTHOR]

In the adaptive cluster design introduced by Thompson (1990), a finite population of units under investigation is partitioned into networks on the basis of a specdied condition for adding neighbourhoods to a sampled unit. An initial sample of units is taken and a network may be sampled more than once. In this paper, we introduce a modification of the design in which networks are sampled only once. Two unbiased estimators are considered and the Rao-Blackwell theorem is used to improve them in terms of efficiency. The various estimators are compared using two examples. [ABSTRACT FROM AUTHOR]