A simple test of Richter-rationality

We propose in this note a simple non-parametric test of Richter-rationality which is the basic definition of rationality used in choice functions theory. Loosely speaking, the data set is rationalizable in the Richter' sense if there exists a complete-acyclic binary relation that rationalizes the data set. Hence a data set is rationalizable in the Richter' sense if there exists a variable intervals function which rationalizes this data set. Since an acyclic binary relation is not necessary transitive then the proposed Richter-rationality test is weaker than GARP. Finally the test is performed over Mattei's data sets.GARP ; choice functions ; Richter-rationality ; variable intervals functions.

Maximum Likelihood Estimation and Uniform Inference with Sporadic Identification Failure

This paper analyzes the properties of a class of estimators, tests, and confidence sets (CS's) when the parameters are not identified in parts of the parameter space. Specifically, we consider estimator criterion functions that are sample averages and are smooth functions of a parameter theta. This includes log likelihood, quasi-log likelihood, and least squares criterion functions. We determine the asymptotic distributions of estimators under lack of identification and under weak, semi-strong, and strong identification. We determine the asymptotic size (in a uniform sense) of standard t and quasi-likelihood ratio (QLR) tests and CS's. We provide methods of constructing QLR tests and CS's that are robust to the strength of identification. The results are applied to two examples: a nonlinear binary choice model and the smooth transition threshold autoregressive (STAR) model.Asymptotic size, Binary choice, Confidence set, Estimator, Identification, Likelihood, Nonlinear models, Test, Smooth transition threshold autoregression, Weak identification

Pulsar Timing Constraints on the Fermi Massive Black-Hole Binary Blazar Population

Blazars are a sub-population of quasars whose jets are nearly aligned with the line-of-sight, which tend to exhibit multi-wavelength variability on a variety of timescales. Quasi-periodic variability on year-like timescales has been detected in a number of bright sources, and has been connected to the orbital motion of a putative massive black hole binary. If this were indeed the case, those blazar binaries would contribute to the nanohertz gravitational-wave stochastic background. We test the binary hypothesis for the blazar population observed by the \textit{Fermi} Gamma-Ray Space Telescope, which consists of BL Lacertae objects and flat-spectrum radio quasars. Using mock populations informed by the luminosity functions for BL Lacertae objects and flat-spectrum radio quasars with redshifts $z \le 2$, we calculate the expected gravitational wave background and compare it to recent pulsar timing array upper limits. The two are consistent only if a fraction $\lesssim 10^{-3}$ of blazars hosts a binary with orbital periods $<5$ years. We therefore conclude that binarity cannot significantly explain year-like quasi-periodicity in blazars.Comment: 5 pages, 4 figures, accepted by MNRAS Letter

Quantum divisibility test and its application in mesoscopic physics

We present a quantum algorithm to transform the cardinality of a set of charged particles flowing along a quantum wire into a binary number. The setup performing this task (for at most N particles) involves log_2 N quantum bits serving as counters and a sequential read out. Applications include a divisibility check to experimentally test the size of a finite train of particles in a quantum wire with a one-shot measurement and a scheme allowing to entangle multi-particle wave functions and generating Bell states, Greenberger-Horne-Zeilinger states, or Dicke states in a Mach-Zehnder interferometer.Comment: 9 pages, 5 figure

Rank-consistent Ordinal Regression for Neural Networks

In many real-world predictions tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, ordinal regression frameworks have been adopted by the deep learning community to take such ordering information into account. Using a framework that transforms ordinal targets into binary classification subtasks, neural networks were equipped with ordinal regression capabilities. However, this method suffers from inconsistencies among the different binary classifiers. We hypothesize that addressing the inconsistency issue in these binary classification task-based neural networks improves predictive performance. To test this hypothesis, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.Comment: In the previous manuscript version, an issue with the figures caused certain versions of Adobe Acrobat Reader to crash. This version fixes this issu