Statistics

1710 Submissions

[4] viXra:1710.0311 [pdf] replaced on 2017-10-31 04:20:07

Human Papilloma Virus - A Cause Of Malignant Melanoma.

Authors: Ilija Barukčić
Comments: 13 pages. Copyright © 2017 by Ilija Barukčić, Jever, Germany. All rights reserved. Published by:

Background: The aim of this study is to evaluate the possible relationship between human papillomavirus (HPV) and malignant melanoma. Objectives: In this systematic review we re-analysed the study of Roussaki-Schulze et al. and the study of La Placa et al. so that some new inferences can be drawn. Materials and methods: Roussaki-Schulze et al. obtained data from 28 human mel-anoma biopsy specimens and from 6 healthy individuals. La Placa et al. investigated 51 primary melanoma (PM) and in 20 control skin samples. The HPV DNA was de-termined by polymerase chain reaction (PCR). Statistical Analysis: The method of the conditio per quam relationship was used to proof the hypothesis whether the presence of human papillomavirus (HPV) guarantees the presence of malignant melanoma. In other words, if human papillomavirus (HPV) is present, then malignant melanoma is present too. The mathematical formula of the causal relationship k was used to proof the hypothesis, whether there is a cause effect relationship between human papillomavirus (HPV) and malignant melanoma. Signifi-cance was indicated by a p-value of less than 0.05. Results: Based on the data as published by Roussaki-Schulze et al. and the data of La Placa et al. the presence of human papillomavirus (HPV) guarantees the presence of malignant melanoma. In other words, human papillomavirus (HPV) is a conditio per quam of malignant melanoma. In contrast to the study of La Placa et al. and contrary to expectation, the study of Roussaki-Schulze et al. which is based on a very small sample size failed to provide evidence of a significant cause effect relationship be-tween human papillomavirus (HPV) and malignant melanoma. Conclusions: Human papillomavirus (HPV) is a necessary condition of malignant melanoma. Human papillomavirus (HPV) is a cause of malignant melanoma.
Category: Statistics

[3] viXra:1710.0261 [pdf] replaced on 2017-12-01 10:20:35

Anomaly Detection and Approximate Matching via Entropy Divergences

Authors: Russell Leidich
Comments: 20 Pages.

The Jensen-Shannon divergence (JSD) quantifies the “information distance” between a pair of probability distributions. (A more generalized version, which is beyond the scope of this paper, is given in [1]. It extends this divergence to arbitrarily many such distributions. Related divergences are presented in [2], which is an excellent summary of existing work.)

A couple of novel applications for this divergence are presented herein, both of which involving sets of whole numbers constrained by some nonzero maximum value. (We’re primarily concerned with discrete applications of the JSD, although it’s defined for analog variables.) The first of these, which we can call the “Jensen-Shannon divergence transform” (JSDT), involves a sliding “sweep window” whose JSD with respect to some fixed “needle” is evaluated at each step as said window moves from left to right across a superset called a “haystack”.

The second such application, which we can call the “Jensen-Shannon exodivergence transform” (JSET), measures the JSD between a sweep window and an “exosweep”, that is, the haystack minus said window, at all possible locations of the latter. The JSET turns out to be exceptionally good at detecting anomalous contiguous subsets of a larger set of whole numbers.

We then investigate and attempt to improve upon the shortcomings of the JSD and the related Kullback-Leibler divergence (KLD).


Category: Statistics

[2] viXra:1710.0243 [pdf] submitted on 2017-10-22 16:38:41

Mathematical Probabilistic Closure

Authors: Paris Samuel Miles-Brenden
Comments: 3 Pages. None.

None.
Category: Statistics

[1] viXra:1710.0029 [pdf] replaced on 2017-11-06 01:31:05

Perturbations of Compressed Data Separation with Redundant Tight Frames

Authors: Feng Zhang, Jianjun Wang, Yao Wang, Jianwen Huang
Comments: 27 Pages.

In the era of big data, the multi-modal data can be seen everywhere. Researchon such data has attracted extensive attention in the past few years. In this paper, we investigate perturbations of compressed data separation with redundant tight frames via ˜Φ-ℓq-minimization. By exploiting the properties of the redundant tight frame and the perturbation matrix, i.e., mutual coherence, null space property and restricted isometry property, the condition on reconstruction of sparse signal with redundant tight frames is established and the error estimation between the local optimal solution and the original signal is also provided. Numerical experiments are carried out to show that ˜Φ-ℓq-minimization are robust and stable for the reconstruction of sparse signal with redundant tight frames. To our knowledge, our works may be the first study concerning perturbations of the measurement matrix and the redundant tight frame for compressed data separation.
Category: Statistics