A simple algorithm for Gaussian mean testing with optimal sample complexity.
problem Distinguishing between standard Gaussian and other Gaussian distributions with unknown mean and covariance.
method An extremely simple algorithm with a one-page analysis.
result Optimal sample complexity of Θ(√d/ε^2) with sample linear time.
Changes in Australian Age Pension mean-tests affect retirement planning and benefits.
problem Policy changes impact optimal retirement decisions and benefits.
method Optimal stochastic control problem in a utility maximizing lifecycle model.
result New rules decrease benefits from means-testing but increase housing allocation slightly.
Unified framework for mean testing under truncation bias.
problem High-dimensional mean testing under arbitrary truncation.
method Characterizes fundamental limits and develops a simple second-order test.
result Unified framework connects finite-moment, sub-Gaussian, and median-regular structural regimes.
Modeling retirement behavior with public pension, optimal consumption and housing decisions.
problem Retirement behavior, consumption, housing, investment, and public pension effects.
method Expected utility model, stochastic control problem, maximum likelihood method.
result Optimal housing, consumption, and risky asset allocation depend on age and wealth, sensitive to means-tested Age Pension.
A method for rank verification in multivariate Gaussian data, improving on existing approaches.
problem Determining the top K means in multivariate Gaussian data with any covariance structure. method Selective inference tools to generalize the two-sided difference-of-means test for any K and covariance structure. result The method provides a generalization for rank verification in multivariate Gaussian data with any covariance structure.
The paper critiques UBI as ineffective for addressing technological unemployment.
problem Technological unemployment due to automation.
method Empirical data analysis and theoretical projections of UBI's impact.
result UBI is not an effective solution for improving living standards and employability among displaced workers.
Spatial machine learning improves poverty targeting in Indonesia.
problem Conventional PMT methods have high exclusion and inclusion errors due to spatial dependencies and regional heterogeneity.
method Integrates spatial contiguity matrices into SML models to identify and compare poverty clusters.
result SML reduces exclusion errors from 28% to 20% compared to standard machine learning models.
Two algorithms improve K-means clustering speed without sacrificing quality.
problem Improving clustering quality of K-means while speeding up the process.
method Divisive K-means and Parallel Two-Phase K-means.
result Achieved empirically global optimum clustering results with lower complexity.
PBA generates nonstationary augmentation schedules to match AutoAugment's performance with less compute.
problem Choosing an effective augmentation policy from a large search space.
method Population Based Augmentation (PBA) generates nonstationary augmentation policy schedules.
result PBA matches AutoAugment's performance on CIFAR-10, CIFAR-100, and SVHN with less compute.
This paper introduces a new method to compare collections of distributions on manifolds and graphs.
problem Comparing collections of probability distributions over diverse domains.
method Intrinsic slicing construction for Wasserstein distances, Hilbert embedding, resampling, p-value combination.
result Powerful and well-calibrated p-values for comparing distributions on manifolds and graphs.
Statistical evaluations of the economic mobility of a society are more difficult than measurements of the income distribution, because they require to follow the evolution of the individuals' income for at least one or two generations. In micro-to-macro theoretical models of economic exchanges based on kinetic equation…
A new algorithm for sequential testing with linear time and space complexity.
problem Sequential hypothesis testing with i.i.d. data.
method Zero-mean martingales and uniform LIL-based stopping criteria.
result Equivalent power to non-sequential tests with smaller factors.
A new method for adaptive experiments improves inference.
problem Current inference methods for adaptive experiments are weak and asymptotic.
method Simulation-based inference using optimistic simulations.
result Our method achieves better coverage and narrower intervals.
The paper analyzes learning curves for kernel ridge regression with dot-product kernels.
problem Understanding the learning curves for different scaling regimes of data and model.
method Precise formulas for mean test error, bias, and variance in the mo∞ with m/dr constant regime. result A peak in the learning curve at m≈dr/r! for any integer r. Proposes a new method to control FDR using frequentist-assisted horseshoe for high-dimensional testing.
problem Designing tests with frequentist false discovery rate control using horseshoe prior.
method Frequentist-assisted horseshoe procedure for high-dimensional normal means testing.
result Consistently achieves robust finite-sample FDR control in various sparse cases.
Robust testing of sparse signals in corrupted data.
problem Testing the norm of high-dimensional sparse signals in the presence of arbitrary corruption.
method Two observation models: i.i.d. samples from N(θ,Id) and sparse linear regression model. result The robust testing requires significantly more samples than non-robust testing.
The paper develops tests for comparing means in high dimensions with unknown covariance.
problem Testing if the mean of a high-dimensional distribution is close to zero or different from another.
method Develops nonasymptotic tests using concentration inequalities and operator norms.
result Obtains bounds on the minimal separation distance for controlling Type I and Type II errors.
Deep neural network optimizes retirement consumption in defined contribution pensions.
problem Optimizing consumption in a defined contribution pension system.
method Formulated as a stochastic control problem, trained deep neural network policy.
result Deep neural network outperforms deterministic rules and adapts to market changes.
Transformers improve Alzheimer's disease progression prediction by accounting for irregular biomarker histories.
problem Difficult prediction of medium-horizon Alzheimer's disease progression due to tied clinical scores and irregular biomarker observations.
method Developed a residual gap-aware transformer that combines statistical reference with transformer-based residual learning.
result The proposed model reduces mean error and improves prediction-observation correlation compared to baseline models.