Harry T. Valentine · Encyclopedia of Environmetrics · 2006
Abstract Randomized branch sampling (RBS) is a special application of multistage probability sampling, which was developed originally by Jessen to estimate fruit counts on individual orchard trees. In general, the method can be used to obtain estimates of many different attributes of trees or other branched plants. The usual objective of RBS is the estimation of a total quantity for a tree or a branch, for example, woody dry matter or volume, leaf dry matter, or counts of tree‐inhabiting insects. RBS also has potential application in the estimation of attributes of river networks and other branched structures.
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2007
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Geir Evensen, Dean S. Oliver, Remus G. Hanea · 2025
Abstract We will now introduce the so-called randomized-maximum-likelihood sampling (RML), which provides a mean for sampling the posterior distribution in cases with modest nonlinearity. The method is a randomized a posteriori sampling, but we will use the standard notation common in the literature. The RML method assumes that we obtain approximate samples from the posterior distribution by minimizing an ensemble of specific cost functions. As this sampling is exact in the linear case, it is reasonable to assume that it provides approximate samples in the weakly nonlinear case. The RML sampling provides a fundamental basis for ensemble methods and allows us to connect these methods directly to Bayes’ theorem.
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Adam Visokay, Laura Boudreau, Rachel Heath et al. · arXiv (Cornell University) · 2026
Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias, particularly under high homophily. We introduce and evaluate Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation of RDS that incorporates researcher-controlled randomization into each recruitment wave. While standard RDS is necessary for stigmatized groups where network transparency is infeasible, RRDS is designed for low-stigma populations that become difficult to access due to logistical barriers. In these contexts, RRDS enforces the random recruitment assumption that traditional RDS relies upon but rarely achieves. Through simulation and an experiment surveying Bangladeshi garment workers during the COVID-19 pandemic, we demonstrate that RRDS produces less biased estimates and improved confidence interval coverage compared to traditional RDS. RRDS offers a scalable, remote-compatible alternative for studying low-stigma groups in challenging contexts where large-scale probability sampling is unsafe or infeasible.
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Maya R. Said, Alan V. Oppenheim · 2002
This paper explores the use of randomized sampling in implementing convolution in discrete-time with application to the areas of approximate filtering, low-power filter design, and hardware failure modeling. Three distinct randomized sampling methods are presented and additive error models as well as second-order error statistics are derived for these for both white and semi-correlated sampling processes. Discrete-time randomized sampling (RS) is then considered as a filter approximation method and conditions are derived under which RS-based approximations to the Wiener filter lead to a smaller mean-square estimation error than the best constrained LTI approximation. The tradeoff between power savings and output quality is also investigated for low-power applications. In addition, the RS framework is used to model a class of random hardware failures and algorithms are presented to improve the output SNR.
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Adam Visokay, Laura Boudreau, Rachel Heath et al. · arXiv (Cornell University) · 2026
Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias, particularly under high homophily. We introduce and evaluate Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation of RDS that incorporates researcher-controlled randomization into each recruitment wave. While standard RDS is necessary for stigmatized groups where network transparency is infeasible, RRDS is designed for low-stigma populations that become difficult to access due to logistical barriers. In these contexts, RRDS enforces the random recruitment assumption that traditional RDS relies upon but rarely achieves. Through simulation and an experiment surveying Bangladeshi garment workers during the COVID-19 pandemic, we demonstrate that RRDS produces less biased estimates and improved confidence interval coverage compared to traditional RDS. RRDS offers a scalable, remote-compatible alternative for studying low-stigma groups in challenging contexts where large-scale probability sampling is unsafe or infeasible.
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Ivars Bilinskis · 2007
This chapter contains sections titled: Periodic Sampling as a Particular Sampling Case Spectra of Sampled Signals Aliasing Induced Errors at Seemingly Correct Sampling Overlapping of Sampled Signal Components Various Approaches to Randomization of Sampling Bibliography
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Sunanda Dey, Alan V. Oppenheim · 2007
This paper explores the use of discrete-time randomized sampling as a method to mitigate the effects of aliasing. Two distinct sampling architectures are presented along with second-order characterizations of the resulting sampling error. In the first, denoted simple randomized sampling, non-white sampling processes are shown to frequency-shape the error spectrum, so that its power is minimized in the band of interest. The design of non-white binary processes for use in randomized sampling is considered. In the second model, denoted filtered randomized sampling, a pre-filter, post-filter, and the sampling process are used to achieve a similar effect. In both cases, optimal mean-squared-error solutions are derived. Results from simulation are shown.
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Henrik Abgaryan, Ararat Harutyunyan, Tristan Cazenave · Lecture notes in computer science · 2025
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Aryan Gupta, Sarthak Gupta, Abhay Kumar et al. · arXiv (Cornell University) · 2023
This paper is a contribution to the reproducibility challenge in the field of machine learning, specifically addressing the issue of certifying the robustness of neural networks (NNs) against adversarial perturbations. The proposed Double Sampling Randomized Smoothing (DSRS) framework overcomes the limitations of existing methods by using an additional smoothing distribution to improve the robustness certification. The paper provides a clear manifestation of DSRS for a generalized family of Gaussian smoothing and a computationally efficient method for implementation. The experiments on MNIST and CIFAR-10 demonstrate the effectiveness of DSRS, consistently certifying larger robust radii compared to other methods. Also various ablations studies are conducted to further analyze the hyperparameters and effect of adversarial training methods on the certified radius by the proposed framework.
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Shaunak D. Bopardikar · arXiv (Cornell University) · 2017
Given a linear dynamical system, we consider the problem of constructing an approximate system using only a subset of the sensors out of the total set such that the observability Gramian of the new system is approximately equal to that of the original system. Our contributions are as follows. First, we present a randomized algorithm that samples the sensors with replacement as per specified distributions. For specific metrics of the observability Gramian such as the trace or the maximum eigenvalue, we derive novel bounds on the number of samples required to yield a high probability lower bound on the metric evaluated on the approximate Gramian. Second, with a different distribution, we derive high probability bounds on other standard metrics used in sensor selection, including the minimum eigenvalue or the trace of the Gramian inverse. This distribution requires a number of samples which is larger than the one required for the trace and the maximum eigenvalue, but guarantees non-singularity of the approximate Gramian if the original system is observable with high probability. Third, we demonstrate how the randomized procedure can be used for recursive state estimation using fewer sensors than the original system and provide a high probability upper bound on the initial error covariance. We supplement the theoretical results with several insightful numerical studies and comparisons with competing greedy approaches.
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Aleksandr Aravkin, Michael P. Friedlander, Felix J. Herrmann et al. · 2011
Abstract We consider a class of inverse problems in which the forward model is the solution operator to linear ODEs or PDEs. This class admits several dimensionality-reduction techniques based on data averaging or sampling, which are especially useful for large-scale problems. We survey these approaches and their connection to stochastic optimization. The data-averaging approach is only viable, however, for a least-squares misfit, which is sensitive to outliers in the data and artifacts unexplained by the forward model. This motivates us to propose a robust formulation based on the Student’s t-distribution of the error. We demonstrate how the corresponding penalty function, together with the sampling approach, can obtain good results for a large-scale seismic inverse problem with 50 % corrupted data. Keywords inverse problems · seismic inversion · stochastic optimization · robust estimation 1
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Ephraim P. Smith, Tead S. Sosnowski · The Journal of Experimental Education · 1972
Students taking part in faculty evaluations have a tendency to avoid questions which reveal too much about themselves when answered. Innocuous questions usually are answered truthfully. Questions that tend to stigmatize usually meet with resistence. Students will try to avoid questions that downgrade them in the eyes of the interviewer. These questions will go unanswered or they will be answered incorrectly. The randomized response model may be employed when evaluating teacher effectiveness. This technique helps eliminate the major causes of bias in sample surveys, that is, the refusal of the respondent to answer questions and incorrect answers. Randomized response allows the interviewee to give answers that furnish information on a probability basis. In that way the interviewer would know only the probability that the answer was true. Since this type of answer is is less revealing, this approach might encourage greater cooperation, better responses, and reduce evasive answer bias.
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Gurjeet Singh Walia · Current Journal of Applied Science and Technology · 2022
In this article, a new estimator for estimating the proportion of sensitive attribute is constructed based on Warner [1] and Singh and Joarder [2] estimators. The results are derived in case respondents are selected by simple random sampling with replacement (SRSWR) and without replacement (SRSWOR). The proposed estimator is unbiased in both the cases and is more efficient than the estimator proposed by Singh and Joarder [2].
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Yanjun Zhang, Hanyu Li, Lin Tang · CALCOLO · 2024
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Jens Rahnfeld (21095109), Mehdi Naouar (18954997), Gabriel Kalweit (11047566) et al. · Figshare · 2025
<div> Recent advancements in deep learning have shown promise in enhancing the performance of medical image analysis. In pathology, automated whole slide imaging has transformed clinical workflows by streamlining routine tasks and diagnostic and prognostic support. However, the lack of transparency of deep learning models, often described as <i>black boxes</i>, poses a significant barrier to their clinical adoption. This study evaluates various explainability methods for Vision Transformers, assessing their effectiveness in explaining the rationale behind their classification predictions on histopathological images. Using a Vision Transformer trained on the publicly available CAMELYON16 dataset comprising of 399 whole slide images of lymph node metastases of patients with breast cancer, we conducted a comparative analysis of a diverse range of state-of-the-art techniques for generating explanations through heatmaps, including Attention Rollout, Integrated Gradients, RISE, and ViT-Shapley. Our findings reveal that Attention Rollout and Integrated Gradients are prone to artifacts, while RISE and particularly ViT-Shapley generate more reliable and interpretable heatmaps. ViT-Shapley also demonstrated faster runtime and superior performance in insertion and deletion metrics. These results suggest that integrating ViT-Shapley-based heatmaps into pathology reports could enhance trust and scalability in clinical workflows, facilitating the adoption of explainable artificial intelligence in pathology. </div>
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Sunanda Dey · DSpace@MIT (Massachusetts Institute of Technology) · 2008
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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Siegfried Gabler, Horst Stenger · Statistics · 2010
Abstract In statistical practice, systematic sampling (SYS) is used in many modifications due to its simple handling. In addition, SYS may provide efficiency gains if it is well adjusted to the structure of the population under study. However, if SYS is based on an inappropriate picture of the population a high decrease of efficiency, i.e. a high increase in variance may result by changing from simple random sampling to SYS. In the context of two-stage designs SYS so far seems often in use for subsampling within the primary units. As an alternative to this practice, we propose to randomize the order of the primary units, then to select systematically a number of primary units and, thereafter, to draw secondary units by simple random sampling without replacement within the primary units selected. This procedure is more efficient than simple random sampling with replacement from the whole population of all secondary units, i.e. the variance of an adequate estimator for a total is never increased by changing from simple random sampling to randomized SYS whatever be the values associated by a characteristic with the secondary units, while there are values for which the variance decreases for the change mentioned. This result should hold generally, even if our proof, so far, is not complete for general sample sizes. Keywords: design effectrandomized systematic samplingvariance generating matrix 2000 AMS Subject Classification : 62D05 Acknowledgements The authors wish to express their thankfulness to the referees who provided many useful suggestions helping us to improve the earlier version of the manuscript.
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Nourmohammadi, Mohammad · Mspace (University of Manitoba) · 2014
In this dissertation, we develop several new inference procedures that are based on randomized nomination sampling (RNS). The first problem we consider is that of constructing distribution-free confidence intervals for quantiles for finite populations. The required algorithms for computing coverage probabilities of the proposed confidence intervals are presented. The second problem we address is that of constructing nonparametric confidence intervals for infinite populations. We describe the procedures for constructing confidence intervals and compare the constructed confidence intervals in the RNS setting, both in perfect and imperfect ranking scenario, with their simple random sampling (SRS) counterparts. Recommendations for choosing the design parameters are made to achieve shorter confidence intervals than their SRS counterparts. The third problem we investigate is the construction of tolerance intervals using the RNS technique. We describe the procedures of constructing one- and two-sided RNS tolerance intervals and investigate the sample sizes required to achieve tolerance intervals which contain the determined proportions of the underlying population. We also investigate the efficiency of RNS-based tolerance intervals compared with their corresponding intervals based on SRS. A new method for estimating ranking error probabilities is proposed. The final problem we consider is that of parametric inference based on RNS. We introduce different data types associated with different situation that one might encounter using the RNS design and provide the maximum likelihood (ML) and the method of moments (MM) estimators of the parameters in two classes of distributions; proportional hazard rate (PHR) and proportional reverse hazard rate (PRHR) models.
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Ethan N. Epperly · ArXiv.org · 2025
Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis for the ARP algorithm and faster implementations using rejection sampling.
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