scholarly journals MODIFICATION OF SCORING SCHEMES USING DECOMPOSITION PROCEDURES ON STATISTICAL DATA

2017 ◽  
Vol 5 ◽  
pp. 627-632
Author(s):  
Monika Hanáková ◽  
Aba Teleki ◽  
Boris LacsnĂ˝
Keyword(s):  
Statistical Data ◽  
Random Variables ◽  
Statistical Evidence ◽  
Independent Random Variables ◽  
Highly Qualified ◽  
Mathematical Background ◽  
Scoring Schemes ◽  
Analytical Tools ◽  
Minor Distortion

This paper presents a method of modifying original scores to obtain independent random variables. It includes an analysis of the consequences of using such a method. The paper also describes the mathematical background of the method in detail and discusses the possible use of the method in identifying student or participant assessments that are over- or underrated. The method distinguishes performances of students and assesses their written solutions using a scoring scheme. In this study, it is used to analyze the competence of participants in the Physics Olympiad competition. Scoring schemes that are appropriately set by an author for a physics problem present the participant scores as independent random variables. The assessment solutions are analyzed using analytical tools (such as covariant matrix) for the dependence of random variables. The evaluators of the participants’ solutions were highly qualified professionals. Nevertheless, the study found statistical evidence of minor distortion in the evaluations, though this was found to only marginally affected the ranking of participants

Basic mathematical imputation techniques

2016 ◽  
Vol 61 (9) ◽  
pp. 7-54
Author(s):  
Jacek Wesołowski ◽  
Jakub Tarczyński
Keyword(s):  
Multiple Imputation ◽  
Random Variables ◽  
Independent Random Variables ◽  
Original Sample ◽  
Variance Estimator ◽  
Mathematical Background ◽  
Random Mechanism ◽  
Starting Point

The article presents the basics of imputation methodology (including the methodology of multiple imputation), focusing on understanding its mathematical background. We analyze the situation when observations in the original sample are independent random variables with identical distributions, and response or its lack is modeled by a random mechanism which is independent of observations. In particular, we point out to problems that arise when the standard Rubin estimate of the multiple imputation variance estimator is used. A possible improvement of this popular estimator is indicated. The starting point of the analysis is when the appearance of response deficiencies is caused by a deterministic mechanism.

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

Methodology of Calculation the Terminal Settling Velocity Distribution of Irregular Particles for High Values of the Reynold’s Number/Metodologia Wyliczania Rozkładu Granicznej Prędkości Opadania Ziaren Nieregularnych Dla Wysokich Wartości Liczb Reynoldsa

2014 ◽  
Vol 59 (2) ◽  
pp. 553-562 ◽  
Author(s):  
Agnieszka Surowiak ◽  
Marian Brożek
Keyword(s):  
Velocity Distribution ◽  
Settling Velocity ◽  
Random Variables ◽  
Independent Random Variables ◽  
Calculation Algorithm ◽  
Shape Coefficient ◽  
Geometrical Properties ◽  
Size And Shape ◽  
Physical Density ◽  
Settling Velocity Distribution

Abstract Settling velocity of particles, which is the main parameter of jig separation, is affected by physical (density) and the geometrical properties (size and shape) of particles. The authors worked out a calculation algorithm of particles settling velocity distribution for irregular particles assuming that the density of particles, their size and shape constitute independent random variables of fixed distributions. Applying theorems of probability, concerning distributions function of random variables, the authors present general formula of probability density function of settling velocity irregular particles for the turbulent motion. The distributions of settling velocity of irregular particles were calculated utilizing industrial sample. The measurements were executed and the histograms of distributions of volume and dynamic shape coefficient, were drawn. The separation accuracy was measured by the change of process imperfection of irregular particles in relation to spherical ones, resulting from the distribution of particles settling velocity.

Ehrenfest urn models

1965 ◽  
Vol 2 (02) ◽  
pp. 352-376 ◽  
Author(s):  
Samuel Karlin ◽  
James McGregor
Keyword(s):  
Exponential Distribution ◽  
Continuous Time ◽  
Random Variables ◽  
Independent Random Variables ◽  
Urn Models ◽  
Time Intervals ◽  
Negative Exponential Distribution ◽  
Negative Exponential ◽  
Random Times

In the Ehrenfest model with continuous time one considers two urns and N balls distributed in the urns. The system is said to be in stateiif there areiballs in urn I, N −iballs in urn II. Events occur at random times and the time intervals T between successive events are independent random variables all with the same negative exponential distributionWhen an event occurs a ball is chosen at random (each of theNballs has probability 1/Nto be chosen), removed from its urn, and then placed in urn I with probabilityp, in urn II with probabilityq= 1 −p, (0 <p< 1).

scholarly journals Some results on the span of families of Banach valued independent, random variables

1991 ◽  
Vol 14 (2) ◽  
pp. 381-384
Author(s):  
Rohan Hemasinha
Keyword(s):  
Banach Space ◽  
Probability Space ◽  
Linear Span ◽  
Random Variables ◽  
Isomorphic Copy ◽  
Independent Random Variables ◽  
Closed Linear Span

LetEbe a Banach space, and let(Ω,ℱ,P)be a probability space. IfL1(Ω)contains an isomorphic copy ofL1[0,1]then inLEP(Ω)(1≤P<∞), the closed linear span of every sequence of independent,Evalued mean zero random variables has infinite codimension. IfEis reflexive orB-convex and1<P<∞then the closed(in LEP(Ω))linear span of any family of independent,Evalued, mean zero random variables is super-reflexive.

scholarly journals Some inequalities of linear combinations of independent random variables: II

Bernoulli ◽  
2013 ◽  
Vol 19 (5A) ◽  
pp. 1776-1789 ◽  
Author(s):  
Xiaoqing Pan ◽  
Maochao Xu ◽  
Taizhong Hu
Keyword(s):  
Random Variables ◽  
Independent Random Variables ◽  
Linear Combinations
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