Exact Tests for in Situ Hybridization Experiments
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Radioactively labeled gene probes are observed as “grains” that identify specific sites of hybridization within chromosomes, suggesting potential sites for a target gene. Based on the resolution of the assay, chromosomes are subdivided into bins of approximately equal length, and the number of grains in each bin is tallied. To distinguish true hybridization sites from nonspecific binding, we propose a test statistic, Z max, based on the number of grains in any two adjacent bins. An efficient algorithm for the exact null distribution of Z max is provided, as well as power calculations and a large-sample approximation. A step-down method that protects the experiment-wide error rate is given to identify multiple hybridization sites.
In this paper, we develop bootstrap methods for factor-augmented regressions with potentially weak common components, allowing signal eigenvalues to diverge at heterogeneous rates $N^{α_k}$, where $0<α_k\leq1$, $k=1,\ldots,r$. The regression estimator targets a rotation of the latent parameter vector that may be data dependent, while factor estimation can induce asymptotic bias. The proposed procedure directly constructs the rotation-specific target in the bootstrap world,…
Mendelian randomization (MR) infers causal effects of exposures on outcomes using genetic variants as instrumental variables, but existing methods remain vulnerable to bias and loss of power when instruments are invalid. We hypothesize that closely related outcome traits share a large overlap in their valid instruments for a given exposure, so that borrowing instruments across such traits can yield more robust MR inference. To operationalize this, we introduce a novel…
Conditional distribution equality testing is a core problem with applications in causal inference, model diagnostics, and transfer learning. Existing methods often lose power when the conditioning variable is high-dimensional. We propose a new test that leverages generative models to compare conditional distributions using both observed data and synthetic samples. A sample-splitting and studentization scheme is introduced to control estimation error. Under the null, the test…
Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on…
Normality pre-tests on reduced-form innovations cannot detect whether two or more underlying structural shocks are Gaussian, and hence cannot detect a failure of identification in non-Gaussian SVARs. We propose a bootstrap-based approach that reveals such a failure. Under the null of valid identification and certain regularity conditions, the conditional bootstrap distribution of the maximum likelihood estimator of the impact matrix is asymptotically normal, so the…
结合 model-X knockoffs 与 p 值的高维线性模型 FDR 控制
We propose a novel multiple testing methodology for controlling the false discovery rate (FDR) in high-dimensional linear models that integrates model-X knockoff techniques with debiased penalized regression estimators. At the foundation of our methodology, we construct and study two sets of naturally paired high-dimensional test statistics and the associated $p$-values for evaluating the same null hypotheses. The first set is shown to be asymptotically mutually independent,…