[3] **viXra:1905.0371 [pdf]**
*submitted on 2019-05-19 06:42:36*

**Authors:** Ilija Barukčić

**Comments:** 29 pages. Copyright © 2019 by Ilija Barukčić, Jever, Germany. All rights reserved. Published by:

Objective: Herbicides are used worldwide by both residential and agricultural users. Due to the statistical analysis of some epidemiologic studies the International Agency for Research on Cancer classified the broad-spectrum herbicide glyphosate (GS) in 2015, as potentially carcinogenic to humans especially with respect to non-Hodgkin lymphoma (NHL). In this systematic review and re-analysis, the relationship between glyphosate and NHL was re- investigated.
Methods: A systematic review and re-analysis of studies which investigated the relationship between GS and NHL was conducted. The method of the conditio sine qua non relationship, the method of the conditio per quam relationship, the method of the exclusion relationship and the mathematical formula of the causal relationship k were used to proof the hypothesis. Significance was indicated by a p-value of less than 0.05.
Results: The studies analyzed do not provide any direct and indirect evidence that NHL is caused GS.
Conclusion: In this re-analysis, no causal relationship was apparent between glyphosate and NHL and its subtypes.
Keywords: Glyphosate, Non-Hodgkin lymphoma, no causal relationship

**Category:** Statistics

[2] **viXra:1905.0345 [pdf]**
*submitted on 2019-05-18 15:08:29*

**Authors:** Idriss olivier BADO

**Comments:** 5 Pages.

in this article we will establish some properties of random variables and then we will propose a conjecture related to the exponential family. This conjecture seems interesting to me. Our results are based on the consideration of continuous random variables $X_{i}$ defined on the same space $\Omega$ and the same super-extra density law of parameter $\theta_{i} $ and canonique function $T$ Let $n\in \mathbb{N}^{*}$
Considering the random variable $J$ and $I$ a subsect of $\{1,2,..n\}$ such that : $ X_{J}=\inf_{i\in I}(X_{i})$ we show that :
$$\forall i\in I:\mathbb{P}( J=i)=\frac{\theta_{i}\prod_{j\in I}c(\theta_{j})}{\sum_{j\in I}\theta_{j}}\int_{T(\Omega)}e^{-x}dx$$. We conjecture that if the density of $ X_{i}$ is $ c(\theta_{i})e^{-\theta_{i}T(x)}\mathbf{1}_{\Omega}(x)$
Hence $\exists h,r$ two functions h such that
$$ \forall i\in I:\mathbb{P}( J=i)=\frac{r(\theta_{i})\prod_{j\in I}h(\theta_{j})}{\sum_{j\in I}r(\theta_{j})}\int_{T(\Omega)}e^{-x}dx$$

**Category:** Statistics

[1] **viXra:1905.0211 [pdf]**
*submitted on 2019-05-14 14:10:00*

**Authors:** Gavin Steininger

**Comments:** 8 Pages.

This paper presents an approach to modelling passive forever churn (i.e., the probability that a user never returns to a game that does not require them to cancel it). The approach is based on parametric mixture models (Weibull, Gamma, and Log-normal) for return times. The model and data are inverted using Bayesian methods (MCMC and DIC) to get parameter estimates, uncertainties, as well as determine the return time distribution for retained users. The inversion scheme is tested on three groups of simulated data sets and one observed data set. The simulated data are generated with each of the parametric models. Each data set is censored to six time horizons, creating 18 data sets. All data sets are inverted with all three parametric models and the DIC is used to select the return time distribution. For all data sets the true return time distribution (i.e., the one that is used to simulate the data) has the best DIC value; for 16 inversions the true return time distribution is found to be significantly better than the other options. For the observed data set inversion, the scheme is able to accurately estimate the \% of users that did return (before the game transitioned into open beta) to given 14 days of observations.

**Category:** Statistics