Lattice-Based Characterizations and Inference for Multivariate Dependence
Abstract
We develop a measure-theoretic framework for conditional multivariate total positivity of order two (cmMTP2), formulated through lattice inequalities without requiring the existence of probability densities. This formulation extends classical density-based notions of positive dependence to general probability measures, including singular distributions. We establish fundamental preservation properties under marginalization, monotone transformations, and weak convergence, thereby providing a unified treatment of dependence structures under distributional limits. We further investigate the behavior of conditional total positivity under Markov-kernel composition and derive characterizations for important copula families. In the Archimedean setting, the defining condition is expressed through log-convexity of generator derivatives, whereas for Gaussian distributions it corresponds to nonpositive off-diagonal entries of the precision matrix, or equivalently, nonnegative partial correlations. To connect these structural results with statistical inference, we introduce empirical lattice contrasts and establish their joint asymptotic normality. The resulting simultaneous testing procedures achieve asymptotic size control and admit a local power analysis. Our results provide a general foundation for density-free characterization, specification testing, and inference concerning positive dependence in multivariate probability models, particularly when conventional density-based methods are inapplicable or insufficient.
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BibTeX
@misc{pramanik2026latticebased,
title = {{Lattice-Based Characterizations and Inference for Multivariate Dependence}},
author = {Paramahansa Pramanik},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14318},
eprint = {2610.14318},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14318},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Lattice-Based Characterizations and Inference for Multivariate Dependence
AU - Paramahansa Pramanik
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.14318
UR - https://arxiv.org/abs/2610.14318
AB - We develop a measure-theoretic framework for conditional multivariate total positivity of order two (cmMTP_2), formulated through lattice inequalities without requiring the existence of probability densities. This formulation extends classical density-based notions of positive dependence to general probability measures, including singular distributions. We establish fundamental preservation properties under marginalization, monotone transformations, and weak convergence, thereby providing a unified treatment of dependence structures under distributional limits. We further investigate the behavior of conditional total positivity under Markov-kernel composition and derive characterizations for important copula families. In the Archimedean setting, the defining condition is expressed through log-convexity of generator derivatives, whereas for Gaussian distributions it corresponds to nonpositive off-diagonal entries of the precision matrix, or equivalently, nonnegative partial correlations. To connect these structural results with statistical inference, we introduce empirical lattice contrasts and establish their joint asymptotic normality. The resulting simultaneous testing procedures achieve asymptotic size control and admit a local power analysis. Our results provide a general foundation for density-free characterization, specification testing, and inference concerning positive dependence in multivariate probability models, particularly when conventional density-based methods are inapplicable or insufficient.
ER -