Papers Survey Sampling
“Survey Sampling” 태그가 달린 논문 15편 · 필터 해제
Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation
Estimating population parameters in finite populations of text documents can be challenging when obtaining the labels for the target variable requires manual annotation. To address this problem, we combine predictions fr…
Survey SamplingA step towards the integration of machine learning and small area estimation
The use of machine-learning techniques has grown in numerous research areas. Currently, it is also widely used in statistics, including the official statistics for data collection (e.g. satellite imagery, web scraping an…
ImputationSurvey SamplingMixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey samplin…
Matrix CompletionNutritionSurveySurvey SamplingDo We Really Even Need Data?
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs, declining survey response rates), researchers increasingly use predic…
Survey SamplingAsymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting. Reusing old data is critical for policy evaluation, but ex…
Multi-Armed BanditsregressionSurvey SamplingvalidSampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
This paper is a tutorial and literature review on sampling algorithms. We have two main types of sampling in statistics. The first type is survey sampling which draws samples from a set or population. The second type is …
Survey SamplingControlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling
Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a popula…
Survey SamplingDesign-unbiased statistical learning in survey sampling
Design-consistent model-assisted estimation has become the standard practice in survey sampling. However, a general theory is lacking so far, which allows one to incorporate modern machine-learning techniques that can le…
BIG-bench Machine LearningLearning TheorySurveySurvey Sampling+1Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big data statistics
Purchase data from retail chains provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the sheer amount of proxy data, the…
SurveySurvey SamplingSubsampling MCMC - An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However…
SurveySurvey SamplingEstimating prediction error for complex samples
With a growing interest in using non-representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generaliz…
PredictionSurveySurvey SamplingThompson SamplingScalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the …
Survey SamplingNetwork driven sampling; a critical threshold for design effects
Web crawling, snowball sampling, and respondent-driven sampling (RDS) are three types of network sampling techniques used to contact individuals in hard-to-reach populations. This paper studies these procedures as a Mark…
ClusteringSurvey SamplingSurvey schemes for stochastic gradient descent with applications to M-estimation
In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natu…
SurveySurvey SamplingLocal Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
We provide a detailed study of the estimation of probability distributions---discrete and continuous---in a stringent setting in which data is kept private even from the statistician. We give sharp minimax rates of conv…
Survey Sampling