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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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8172533 · Apr 202019922001200920172026
48 results for survey

Study shows non-systematic bias in customer satisfaction surveys limits data value.

problem Non-systematic bias in customer satisfaction surveys limits data value.
method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.
result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.

The paper tests the credibility of public and private surveys using linear regression and differential privacy.

problem Ensuring the validity of data analysis results from sample surveys using linear regression.
method Designing an algorithm to test the credibility of surveys and extending it to handle LDP.
result The algorithm achieves optimal estimation error bound for 1\ell_1 linear regression and reduces sample complexity.

This is a survey of motivations, constructions and applications of higher prequantum geometry. In section 1 we highlight the open problem of prequantizing local field theory in a local and gauge invariant way, and we survey how a solution to this problem exists in higher differential geometry. In section 2 we survey ex…

2016-01-22abs ↗pdf ↗

New ensemble models classify mouse movement trajectories to assess survey question difficulty.

problem Assessing survey question difficulty based on respondents' interaction data.
method Ensemble models combining semi-metric-based weak learners to classify multivariate functional data.
result Improved survey data quality through better identification of respondent difficulty.

Digital personas improve survey results for stable attributes but fail for subjective responses.

problem When can digital personas reliably approximate human survey findings?
method Using LISS panel, constructed personas from background variables and survey histories, tested against held-out post-cutoff answers.
result Digital personas improve alignment with human response distributions for stable attributes but fail for subjective responses.

Survey of methods for solving smooth stochastic variational inequalities.

problem Solving smooth (strongly) monotone stochastic variational inequalities.
method Deterministic foundation, general stochastic formulation, finite sum setup, recent advances.
result Review of various methods for solving smooth stochastic variational inequalities.

This survey covers in our opinion the most important results in the theory of continuous selections of multivalued mappings (approximately) from 2002 through 2012. It extends and continues our previous such survey which appeared in Recent Progress in General Topology, II, which was published in 2002. In comparison, our…

2014-01-10abs ↗pdf ↗

SDRF estimates complex survey designs for conditional distributions.

problem Estimating conditional distributions under complex survey designs.
method Survey-calibrated distributional random forest (SDRF) with pseudo-population bootstrap and MMD split criterion.
result Established design consistency and model consistency for survey designs.

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order …

2016-07-14abs ↗pdf ↗

Surveying machine learning methods for economic forecasting.

problem Improving accuracy of economic forecasts using machine learning.
method Nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
result Recent advances in machine learning methods enhance economic forecasting accuracy.

Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.

problem Improving transportation network service by detecting, predicting, and alleviating traffic congestion.
method Comprehensive review of deep learning applications in transportation, focusing on challenges specific to congestion.
result Identification of challenges and gaps in current research on deep learning for traffic congestion.