537 citations · 887 across the 42 of their papers we have counts for
13 papers · 1 filter
Strong Approximations for Empirical Processes Indexed by Lipschitz Functions
Matias D. Cattaneo, Ruiqi Rae Yu
This paper presents new uniform Gaussian strong approximations for empirical processes indexed by classes of functions based on -variate random vectors (). First, a unif…
On Rosenbaum's Rank-based Matching Estimator
Matias D. Cattaneo, Fang Han, Zhexiao Lin
In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate s…
Inference with Mondrian Random Forests
Matias D. Cattaneo, Jason M. Klusowski, William G. Underwood
Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondri…
Bootstrap-Assisted Inference for Generalized Grenander-type Estimators
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
Westling and Carone (2020) proposed a framework for studying the large sample distributional properties of generalized Grenander-type estimators, a versatile class of nonparametric…
Convergence Rates of Oblique Regression Trees for Flexible Function Libraries
Matias D. Cattaneo, Rajita Chandak, Jason M. Klusowski
We develop a theoretical framework for the analysis of oblique decision trees, where the splits at each decision node occur at linear combinations of the covariates (as opposed to…
Yurinskii's Coupling for Martingales
Matias D. Cattaneo, Ricardo P. Masini, William G. Underwood
Yurinskii's coupling is a popular theoretical tool for non-asymptotic distributional analysis in mathematical statistics and applied probability, offering a Gaussian strong approxi…
Boundary Adaptive Local Polynomial Conditional Density Estimators
Matias D. Cattaneo, Rajita Chandak, Michael Jansson +1
We begin by introducing a class of conditional density estimators based on local polynomial techniques. The estimators are boundary adaptive and easy to implement. We then study th…
Uniform Inference for Kernel Density Estimators with Dyadic Data
Matias D. Cattaneo, Yingjie Feng, William G. Underwood
Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many oth…
Large Sample Properties of Partitioning-Based Series Estimators
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
We present large sample results for partitioning-based least squares nonparametric regression, a popular method for approximating conditional expectation functions in statistics, e…
Bootstrap-Based Inference for Cube Root Asymptotics
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting a Chernoff (1964)-type limiting distribution. For estimators of this kind, the…
On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference
Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Nonparametric methods play a central role in modern empirical work. While they provide inference procedures that are more robust to parametric misspecification bias, they may be qu…
Inference in Linear Regression Models with Many Covariates and Heteroskedasticity
Matias D. Cattaneo, Michael Jansson, Whitney K. Newey
The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model s…
Alternative Asymptotics and the Partially Linear Model with Many Regressors
Matias D. Cattaneo, Michael Jansson, Whitney K. Newey
Non-standard distributional approximations have received considerable attention in recent years. They often provide more accurate approximations in small samples, and theoretical i…