scholarly journals Statistical Tests for Diagnosing Fission Source Convergence and Undersampling in Monte Carlo Criticality Calculations

2020 ◽  
Author(s):  
Forrest B. Brown
Keyword(s):  
Monte Carlo ◽  
Statistical Tests ◽  
Source Convergence

scholarly journals Statistical Tests of Symbolic Dynamics

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 817
Author(s):  
Fernando López ◽  
Mariano Matilla-García ◽  
Jesús Mur ◽  
Manuel Ruiz Marín
Keyword(s):  
Monte Carlo ◽  
Symbolic Dynamics ◽  
Null Hypothesis ◽  
Statistical Tests ◽  
General Applicability ◽  
Monte Carlo Experiments ◽  
The Mean ◽  
Theoretical Application ◽  
New Statistics ◽  
General Method

A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test.

Statistical tests of differential susceptibility: Performance, limitations, and improvements

2017 ◽  
Vol 29 (4) ◽  
pp. 1267-1278 ◽  
Author(s):  
Marco Del Giudice
Keyword(s):  
Monte Carlo ◽  
Statistical Approach ◽  
Statistical Tests ◽  
Differential Susceptibility ◽  
Initial Response ◽  
Empirical Literature ◽  
False Negatives ◽  
Interaction Index ◽  
Future Studies ◽  
Highly Sensitive

AbstractStatistical tests of differential susceptibility have become standard in the empirical literature, and are routinely used to adjudicate between alternative developmental hypotheses. However, their performance and limitations have never been systematically investigated. In this paper I employ Monte Carlo simulations to explore the functioning of three commonly used tests proposed by Roisman et al. (2012). Simulations showed that critical tests of differential susceptibility require considerably larger samples than standard power calculations would suggest. The results also showed that existing criteria for differential susceptibility based on the proportion of interaction index (i.e., values between .40 and .60) are especially likely to produce false negatives and highly sensitive to assumptions about interaction symmetry. As an initial response to these problems, I propose a revised test based on a broader window of proportion of interaction index values (between .20 and .80). Additional simulations showed that the revised test outperforms existing tests of differential susceptibility, considerably improving detection with little effect on the rate of false positives. I conclude by noting the limitations of a purely statistical approach to differential susceptibility, and discussing the implications of the present results for the interpretation of published findings and the design of future studies in this area.

Source extrapolation scheme for Monte Carlo fission source convergence based on RMC code

2022 ◽  
Vol 166 ◽  
pp. 108737
Author(s):  
Qingquan Pan ◽  
Yun Cai ◽  
Lianjie Wang ◽  
Tengfei Zhang ◽  
Xiaojing Liu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Source Convergence ◽  
Extrapolation Scheme

On-Line Monitoring the Stationarity for Time Seires

2013 ◽  
Vol 444-445 ◽  
pp. 687-691
Author(s):  
Zhan Shou Chen
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Statistical Tests ◽  
Variance Ratio ◽  
Test Power ◽  
Normal Sequence ◽  
Fixed Sample ◽  
Monte Carlo Studies ◽  
Monitoring Procedure ◽  
On Line

When analyzing time series an important issue is to decide whether the time series is stationary or nonstationary. Fixed sample statistical tests for that problem are well studies in the literature. In this paper we propose a moving variance ratio statistic to monitor the stationarity for normal sequence. Our Monte Carlo studies show that the proposed monitoring procedure has satisfactory test power and that the decision can often be made very early.

Approximate upper bounds for the critical probability of oriented percolation in two dimensions based on rapidly mixing Markov chains

1997 ◽  
Vol 34 (04) ◽  
pp. 859-867
Author(s):  
Béla Bollabás ◽  
Alan Stacey
Keyword(s):  
Monte Carlo ◽  
Markov Chains ◽  
Monte Carlo Simulations ◽  
Statistical Tests ◽  
Upper Bounds ◽  
Two Dimensions ◽  
Oriented Percolation ◽  
Critical Probability ◽  
Confidence Levels

We develop a technique for establishing statistical tests with precise confidence levels for upper bounds on the critical probability in oriented percolation. We use it to give pc < 0.647 with a 99.999967% confidence. As Monte Carlo simulations suggest that pc ≈ 0.6445, this bound is fairly tight.

scholarly journals A Simple Monte Carlo Framework to Assess Suicide Risk in Adolescents: A Study at a High School in Colombia

2020 ◽  
Vol 17 (10) ◽  
pp. 3674
Author(s):  
Elias David Nino-Ruiz ◽  
Ana Maria Trejos-Herrera ◽  
Maria Yaquelin Exposito-Concepcion ◽  
Marjorie Rodriguez-Giraldo ◽  
Randy Steven Consuegra-Ortega ◽  
...  
Keyword(s):  
High School ◽  
Monte Carlo ◽  
Markov Chain ◽  
Suicidal Ideation ◽  
Suicide Risk ◽  
Eating Behaviors ◽  
Eating Habits ◽  
Statistical Tests ◽  
Joint Probability ◽  
Psychological Constructs

It is very common to perform statistical tests to obtain insights about populations based on samples. For instance, in the context of psychology, when a set of instruments are applied to individuals, psychologists typically look for an explanation of particular psychological constructs (variables), such as personality, intelligence, or emotional functioning. It is common to cross statistical information from the results of different psychological tests to measure certain variables or to confirm prior beliefs. Here, we estimate the Joint Probability Density Function of suicide-related vulnerability and protective factors to assess suicide risk in adolescents. A Markov Chain Monte Carlo Method is employed to move away from the typical Gaussian assumption on data. This allows us to estimate probabilities of the development of suicidal ideation based on samples (which form a Markov chain). We employ our proposed statistical method at a high school in Colombia. The results reveal that adolescents can develop suicidal ideation as a consequence of the following factors, together with their corresponding probabilities: poor school performance 52%, low academic expectations 27%, school integration problems 68%, risky eating behaviors (binge-purge) 42%, risky eating behaviors (compensatory measurements) 21%, risky eating habits (restriction) 22%, and low family functionality 16%.

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