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Found 9 papers, 6 papers with code
The Projected Covariance Measure for assumption-lean variable significance testing
In this work we study the problem of testing the model-free null of conditional mean independence, i. e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$.
Cross-validation for change-point regression: pitfalls and solutions
We show that in fact the problems of cross-validation with squared error loss are more severe and can lead to systematic under- or over-estimation of the number of change-points, and highly suboptimal estimation of the mean function in simple settings where changes are easily detectable.
High-dimensional regression with potential prior information on variable importance
Examples include ordering on the variables offered by their empirical variances (which is typically discarded through standardisation), the lag of predictors when fitting autoregressive models in time series settings, or the level of missingness of the variables.
Structure Learning for Directed Trees
Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model.
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis
We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables.
Statistics Theory Statistics Theory
Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties
We provide an algorithm for exact and efficient computation of the global minimum of the resulting nonconvex objective in the case with a single variable with potentially many levels, and use this within a block coordinate descent procedure in the multivariate case.
Goodness-of-fit testing in high-dimensional generalized linear models
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model.
Methodology Statistics Theory Statistics Theory
The xyz algorithm for fast interaction search in high-dimensional data
When performing regression on a dataset with $p$ variables, it is often of interest to go beyond using main linear effects and include interactions as products between individual variables.
On b-bit min-wise hashing for large-scale regression and classification with sparse data
Large-scale regression problems where both the number of variables, $p$, and the number of observations, $n$, may be large and in the order of millions or more, are becoming increasingly more common.
