Search for: All records

Abstract In some high-dimensional and semiparametric inference problems involving two populations, the parameter of interest can be characterized by two-sample U-statistics involving some nuisance parameters. In this work we first extend the framework of one-step estimation with cross-fitting to two-sample U-statistics, showing that using an orthogonalized influence function can effectively remove the first order bias, resulting in asymptotically normal estimates of the parameter of interest. As an example, we apply this method and theory to the problem of testing two-sample conditional distributions, also known as strong ignorability. When combined with a conformal-based rank-sum test, we discover that the nuisance parameters can be divided into two categories, where in one category the nuisance estimation accuracy does not affect the testing validity, whereas in the other the nuisance estimation accuracy must satisfy the usual requirement for the test to be valid. We believe these findings provide further insights into and enhance the conformal inference toolbox. 

Speleothem δ¹⁸O records from central southern China have long been regarded as a key benchmark for Asian summer monsoon intensity. However, the similar δ¹⁸O minima observed among precession minima and their link to seasonal precipitation mixing remains unclear. Here, we present a 400,000-y record of summer precipitation δ¹⁸O from loess microcodium, which captures distinct precession cycles similar to those seen in speleothem δ¹⁸O records, particularly during glacial periods. Notably, our microcodium δ¹⁸O record reveals very low-δ¹⁸O values during precession minima at peak interglacials, a feature absent in speleothem δ¹⁸O records from central southern China. This discrepancy suggests that the mixed summer and nonsummer climatic signals substantially influence the speleothem δ¹⁸O records from central southern China. Proxy-model comparisons indicate that the lack of very low-δ¹⁸O values in speleothem δ¹⁸O records is due to an attenuated summer signal contribution, resulting from a lower summer-to-annual precipitation ratio in southern China at strong monsoon intervals. Our findings offer a potential explanation for the long-standing puzzle of the absence of 100- and 41-kyr cycles in speleothem δ¹⁸O records and underscore the critical role of seasonality in interpreting paleoclimatic proxies in central southern China. These insights also have broader implications for interpreting speleothem δ¹⁸O records globally, advocating for a more multiseason interpretive framework. 

ABSTRACT In genomics studies, the investigation of gene relationships often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for statisticians because gene relationships are often local. They change from one sample point to another, may only exist in a subset of the sample, and can be nonlinear or even nonmonotone. Most previous dependence measures do not specifically target local dependence relationships, and the ones that do are computationally costly. In this paper, we explore a state-of-the-art network estimation technique that characterizes gene relationships at the single cell level, under the name of cell-specific gene networks. We first show that averaging the cell-specific gene relationship over a population gives a novel univariate dependence measure, the averaged Local Density Gap (aLDG), that accumulates local dependence and can detect any nonlinear, nonmonotone relationship. Together with a consistent nonparametric estimator, we establish its robustness on both the population and empirical levels. Then, we show that averaging the cell-specific gene relationship over mini-batches determined by some external structure information (eg, spatial or temporal factor) better highlights meaningful local structure change points. We explore the application of aLDG and its minibatch variant in many scenarios, including pairwise gene relationship estimation, bifurcating point detection in cell trajectory, and spatial transcriptomics structure visualization. Both simulations and real data analysis show that aLDG outperforms existing ones.