Concordance measures for multivariate non-continuous random vectors

A Monte Carlo method for calculating strength functions in many-fermion systems

We present experimental findings on the decoding failure rate (DFR) of BIKE, a fourth-round candidate in the NIST Post-Quantum Standardization process, at the 20-bit security level. We select parameters according to BIKE design principles and conduct a series of experiments. We directly compute the average DFR on a range of BIKE block sizes and identify both the waterfall and error floor regions of the DFR curve. We then study the influence on the average DFR of three sets \(\mathcal {C}\), \(\mathcal {N}\), and \(2\mathcal {N}\) of near-codewords—vectors of low weight that induce syndromes of low weight—defined by Vasseur in 2021. We find that error vectors leading to decoding failures have small maximum support intersection with elements of these sets; further, the distribution of intersections is quite similar to that of sampling random error vectors and counting the intersections with \(\mathcal {C}\), \(\mathcal {N}\), and \(2\mathcal {N}\). Our results indicate that these three sets are not sufficient in classifying vectors expected to cause decoding failures. Finally, we study the role of syndrome weight on the decoding behavior and conclude that the set of error vectors that lead to decoding failures differ from random vectors by having low syndrome weight.KeywordsBIKEError-correcting codesMcEliecePQCQC-MDPC

A useful property of independent samples is that their correlation remains the same after applying marginal transforms. This invariance property plays a fundamental role in statistical inference, but does not hold in general for dependent samples. In this paper, we study this invariance property on the Pearson correlation coefficient and its applications. A multivariate random vector is said to have an invariant correlation if its pairwise correlation coefficients remain unchanged under any common marginal transforms. For a bivariate case, we characterize all models of such a random vector via a certain combination of comonotonicity—the strongest form of positive dependence—and independence. In particular, we show that the class of exchangeable copulas with invariant correlation is precisely described by what we call positive Fréchet copulas. In the general multivariate case, we characterize the set of all invariant correlation matrices via the clique partition polytope. We also propose a positive regression dependent model that admits any prescribed invariant correlation matrix. Finally, we show that all our characterization results of invariant correlation, except one special case, remain the same if the common marginal transforms are confined to the set of increasing ones.

1. The main notions.- 2. The main lemmas.- 2.1. General lemmas on the approximation of distribution of an arbitrary random variable by the normal distribution.- 2.2. Proof of lemmas 2.1-2.4.- 3. Theorems on large deviations for the distributions of sums of independent random variables.- 3.1. Theorems on large deviations under Bernstein's condition.- a) Sums of non-identically distributed random variables.- b) Sums of weighted random variables.- 3.2. A theorem of large deviations in terms of Lyapunov's fractions.- 4. Theorems of large deviations for sums of dependent random variables.- 4.1. Estimates of the kth order centered moments of random processes with mixing.- 4.2. Estimates of mixed cumulants of random processes with mixing.- 4.3. Estimates of cumulants of sums of dependent random variables.- 4.4. Theorems and inequalities of large deviations for sums of dependent random variables.- 5. Theorems of large deviations for polynomial forms, multiple stochastic integrals and statistical estimates.- 5.1. Estimates of cumulants and theorems of large deviations for polynomial forms, polynomial Pitman estimates and U-statistics.- 5.2. Cumulants of multiple stochastic integrals and theorems of large deviations.- 5.3. Large deviations for estimates of the spectrum of a stationary sequence.- 6. Asymptotic expansions in the zones of large deviations.- 6.1. Asymptotic expansion for distribution density of an arbitrary random variable.- 6.2. Estimates for characteristic functions.- 6.3. Asymptotic expansion in the Cramer zone for distribution density of sums of independent random variables.- 6.4. Asymptotic expansions in integral theorems with large deviations.- 7. Probabilities of large deviations for random vectors.- 7.1. General lemmas on large deviations for a random vector with regular behaviour of cumulants.- 7.2. Theorems on large deviations for sums of random vectors and quadratic forms.- a) Sums of non-identically distributed random vectors.- b) Sums of weighted random vectors.- c) Sums of random number of random vectors.- d) Quadratic forms.- Appendices.- Appendix 1. Proof of inequalities for moments and Lyapunov's fractions.- Appendix 2. Proof of the lemma on the representation of cumulants.- Appendix 3. Leonov - Shiryaev's formula.- References.

Most everyday reasoning and decision making is based on uncertain premises. The premises or attributes, which we must take into consideration, are random variables, therefore we often have to deal with a high dimensional multivariate random vector. A multivariate random vector can be represented graphically as a Markov network. Usually the structure of the Markov network is unknown. In this paper we construct special type of junction trees, in order to obtain good approximations of the real probability distribution. These junction trees are capable of revealing some of the conditional independences of the network. We have already introduced the concept of the t-cherry junction tree (E. Kovacs and T. Szantai in Proceedings of the IFIP/IIASA//GAMM Workshop on Coping with Uncertainty, 2010), based on the t-cherry tree graph structure. This approximation uses only two and three dimensional marginal probability distributions. Now we use k-th order t-cherry trees, also called simplex multitrees to introduce the concept of the k-th order t-cherry junction tree. We prove that the k-th order t-cherry junction tree gives the best approximation among the family of k-width junction trees. Then we give a method which starting from a k-th order t-cherry junction tree constructs a (k+1)-th order t-cherry junction tree which gives at least as good approximation. In the last part we present some numerical results and some possible applications.

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

In human genetics, twin studies are widely underwent for investigation of the genetic influence on diseases. There are several measures that had been proposed to evaluate the similarity between two twins for dichotomous traits when the twins are sampled at random. These measures include the correlations, odds ratios, and casewise and pairwise concordances. When data are sampled through an ascertainment procedure, truncated data are formed. Under this circumstance, odds ratio cannot be defined and correlations cannot be correctly estimated. However, concordance measures for dichotomous traits can still be estimated using the likelihood method. On the other hand, though theoretically concordance measures can be extended to trichotomous traits, how to define them and to derive their estimators for ascertained trichotomous traits data have not been thoroughly discussed. In this study, we aim to address several relevant issues for ascertained trichotomous traits data. We define two new (casewise and pairwise) concordance measures for trichotomous traits and demonstrate how to apply a so-called 'self-contained subsets method (SCSM)' to estimation of twin concordances for ascertained data. We show that this method can obtain the same estimates as the likelihood method in an easier way and derive the asymptotic variances of the SCSM estimates under ascertainment. We establish the testing procedure for test of the equality of concordance measures between monozygotic twin pairs and dizygotic twin pairs, and illustrate the methods with a real data set and conduct Monte Carlo simulation to investigate its power performance.

Many populations of early-stage cancer patients have non-negligible latent cure fractions that can be modeled using transformation cure models. However, there is a lack of statistical metrics to evaluate prognostic utility of biomarkers in this context due to the challenges associated with unknown cure status and heavy censorship. In this article, we develop general concordance measures as evaluation metrics for the discriminatory accuracy of transformation cure models including the so-called promotion time cure models and mixture cure models. We introduce explicit formulas for the consistent estimates of the concordance measures, and show that their asymptotically normal distributions do not depend on the unknown censoring distribution. The estimates work for both parametric and semiparametric transformation models as well as transformation cure models. Numerical feasibility of the estimates and their robustness to the censoring distributions are illustrated via simulation studies and demonstrated using a melanoma data set.

Pacific Decadal Oscillation (PDO) index is an important indicator of climate variability. However, large discrepancies are found among real‐time PDO monitoring indices maintained by several operational centers, with larger uncertainty exhibiting prior to 1950s and after late 1990s on seasonal to decadal time scales. Two historical sea surface temperature (SST) data sets are used to investigate causes for the uncertainty: the Extended Reconstructed Sea Surface Temperature version 3b (ERSST 3b) and the Hadley Center Sea Ice and SST data set (HadISST) version 1. It is found that choices of spatial structure of Empirical Orthogonal Function (EOF) vector and SST data set are important sources of uncertainty on seasonal to decadal time scales, while choice of climatological base period only contributes to uncertainty on seasonal time scale. Decadal variation of differences in PDO indices from ERSST and HadISST is associated with systematic differences between the two data sets in the central and the north‐eastern Pacific.

GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game

Talagrand’s inductive method and isoperimetric inequalities involving random sets

The concordance correlation coefficient (CCC), a measure of concordance in ratings from multiple raters, was used to study inter-rater agreement in measurements of time to event, generally not observed with perfect consistency among raters. As a function of the first two moments of rating measures, the CCC can be estimated with data subject to censoring, using a likelihood-based estimation method applied under the assumptions of random censoring and parametric distribution models for the ratings of time to event. A simulation study was conducted for small sample performance under various censoring proportions. The use of the CCC with censored data is illustrated with an example taken from a data set containing data on time to an event with two raters per subject.

This paper presents an approach for handling correlated input variables in discrete event simulation (DES) modelling of truck–shovel systems using commercial DES software and uses a case study to investigate the effect of ignoring correlation between input variables. Multivariate random vectors, instead of independent probability distributions, are used for variables found to be correlated. The authors prove that correlations do exist in truck–shovel haulage systems. The model with multivariate random vectors performs better than the original model. The significance of modelling correlation in input variables depends on the strength of the correlation and the output’s sensitivity to the input variables.

Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets

Remarks on univariate elliptical distributions