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In conditional copula models, the copula parameter is deterministically linked to a covariate via the calibration function. The latter is of central interest for inference and is usually estimated nonparametrically. However, when a…

Methodology · Statistics 2014-03-19 Elif F. Acar , Radu V. Craiu , Fang Yao

Wick polynomials and Wick products are studied in the context of non-commutative probability theory. It is shown that free, boolean and conditionally free Wick polynomials can be defined and related through the action of the group of…

Probability · Mathematics 2021-10-05 K. Ebrahimi-Fard , F. Patras , N. Tapia , L. Zambotti

In many high-dimensional problems,polynomial-time algorithms fall short of achieving the statistical limits attainable without computational constraints. A powerful approach to probe the limits of polynomial-time algorithms is to study the…

Statistics Theory · Mathematics 2025-07-11 Bertrand Even , Christophe Giraud , Nicolas Verzelen

In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by a recursive (or acyclic) system of linear structural equations. These linear models have a long tradition and…

Statistics Theory · Mathematics 2010-03-04 Mathias Drton , Michael Eichler , Thomas S. Richardson

This paper studies estimation in functional linear quantile regression in which the dependent variable is scalar while the covariate is a function, and the conditional quantile for each fixed quantile index is modeled as a linear functional…

Statistics Theory · Mathematics 2013-02-28 Kengo Kato

In this thesis we study convolutions that arise from noncommutative probability theory. We prove several regularity results for free convolutions, and for measures in partially defined one-parameter free convolution semigroups. We discuss…

Operator Algebras · Mathematics 2007-05-23 Serban Teodor Belinschi

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of…

Artificial Intelligence · Computer Science 2011-05-30 J. Rintanen

By viewing non-commutative polynomials, that is, elements in free associative algebras, in terms of linear representations, we generalize Horner's rule to the non-commutative (multivariate) setting. We introduce the concept of Horner…

Rings and Algebras · Mathematics 2019-10-04 Konrad Schrempf

We analyze selected iterated conditionals in the framework of conditional random quantities. We point out that it is instructive to examine Lewis's triviality result, which shows the conditions a conditional must satisfy for its probability…

Probability · Mathematics 2020-03-17 Giuseppe Sanfilippo , Angelo Gilio , David Over , Niki Pfeifer

Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compositional is limited and the existing models are difficult to…

Methodology · Statistics 2020-04-20 Jacob Fiksel , Scott Zeger , Abhirup Datta

Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test…

Statistics Theory · Mathematics 2024-05-08 Anton Rask Lundborg , Ilmun Kim , Rajen D. Shah , Richard J. Samworth

It has been recently shown in Jaworski, P., Jelito, D. and Pitera, M. (2024), 'A note on the equivalence between the conditional uncorrelation and the independence of random variables', Electronic Journal of Statistics 18(1), that one can…

Methodology · Statistics 2024-06-24 Kewin Pączek , Damian Jelito , Marcin Pitera , Agnieszka Wyłomańska

In this paper, we develop the notion of free-Boolean independence in an amalgamation setting. We construct free-Boolean cumulants and show that the vanishing of mixed free-Boolean cumulants is equivalent to our free-Boolean independence…

Operator Algebras · Mathematics 2017-12-08 Weihua Liu , Ping Zhong

Using the theory of Hilbert direct integrals, we introduce and study a monotonicity-preserving operation, termed the integral resolvent mixture. It combines arbitrary families of monotone operators acting on different spaces and linear…

Optimization and Control · Mathematics 2024-08-13 Minh N. Bùi , Patrick L. Combettes

Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian non-parametric method to…

Methodology · Statistics 2021-10-22 Steven G. Xu , Brian J. Reich

We consider two nonparametric approaches to ensure that linear instrumental variables estimators satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned…

Econometrics · Economics 2025-07-24 Ludgero Glorias , Federico Martellosio , J. M. C. Santos Silva

We aim to completely formalize the rough topological analysis of integrable Hamiltonian systems admitting analytical solutions such that the initial phase variables along with the time derivatives of the auxiliary variables are expressed as…

Exactly Solvable and Integrable Systems · Physics 2013-09-30 Mikhail P. Kharlamov

Unlike classical and free independence, the boolean and monotone notions of independence lack of the property of independent constants. In the scalar case, this leads to restrictions for the central limit theorems, as observed by F.…

Probability · Mathematics 2021-09-14 Carlos Dias-Aguilera , Tulio Gaxiola , Jorge Santos , Carlos Vargas

Instrumental variable methods are widely used for inferring the causal effect in the presence of unmeasured confounders. Existing instrumental variable methods for nonlinear outcome models require stringent identifiability conditions. This…

Methodology · Statistics 2022-07-01 Sai Li , Zijian Guo

We consider the composition optimization with two expected-value functions in the form of $\frac{1}{n}\sum\nolimits_{i = 1}^n F_i(\frac{1}{m}\sum\nolimits_{j = 1}^m G_j(x))+R(x)$, { which formulates many important problems in statistical…

Machine Learning · Statistics 2017-10-27 Liu Liu , Ji Liu , Dacheng Tao