Non-parametric -sample tests: Density functions vs distribution functions

2009 ◽  
Vol 53 (9) ◽  
pp. 3344-3357 ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Jacobo de Uña-Álvarez
Keyword(s):  
Distribution Functions ◽  
Density Functions ◽  
Non Parametric

Non-parametric e-mixture of Density Functions

2016 ◽  
pp. 3-10 ◽  
Author(s):  
Hideitsu Hino ◽  
Ken Takano ◽  
Shotaro Akaho ◽  
Noboru Murata
Keyword(s):  
Density Functions ◽  
Non Parametric

scholarly journals Comments on Copula Functions and Their Relationship to Probability Density Functions

2020 ◽  
pp. 1115-1122
Author(s):  
Ahmed AL-Adilee ◽  
Ola Hassan
Keyword(s):  
Probability Density ◽  
Statistical Inference ◽  
Joint Distribution ◽  
Probability Density Functions ◽  
Distribution Functions ◽  
Density Functions ◽  
Copula Functions ◽  
Statistical Inferences ◽  
Dependence Structures

Copulas are very efficient functions in the field of statistics and specially in statistical inference. They are fundamental tools in the study of dependence structures and deriving their properties. These reasons motivated us to examine and show  various types of copula functions and their families. Also, we separately explain each method that is used to construct each copula in detail with different examples. There are various outcomes that show the copulas and their densities with respect to the joint distribution functions. The aim is to make copulas available to new researchers and readers who are interested in the modern phenomenon of statistical inferences.

Modeling fabric development using coupled non-parametric orientation and lattice strain-energy distribution functions

2020 ◽  
Author(s):  
Nicholas Rathmann ◽  
Aslak Grindsted ◽  
Sérgio H. Faria ◽  
David A. Lilien ◽  
Christine S. Hvidberg ◽  
...  
Keyword(s):  
Strain Energy ◽  
Lattice Strain ◽  
Cold Work ◽  
Ice Core ◽  
Distribution Functions ◽  
Ductile Deformation ◽  
Model Parameters ◽  
Axis Orientation ◽  
Distribution Shape ◽  
Non Parametric

<p><span>As polycrystalline ice undergoes ductile deformation, the c-axis fabric develops and the effective, macroscopic physical properties of ice become anisotropic. Modeling the flow of anisotropic ice therefore necessitates modeling the evolution of c-</span><span>axes</span><span>, too. We propose a non-parametric spectral model to account for the co-evolution of c-axis orientation distributions and stored lattice strain-energy distributions, </span>which in principle allows any distribution shape to be represented<span>. The coupled evolution provides the means to (statistically) model nucleation and m</span><span>igration recrystallization</span><span> in an energy consistent way as nonuniform decay processes that depend on the accumulated cold work experienced by a given parcel of ice. The free model parameters determine the relative importance of </span><span>grain </span><span>rotation versus dynamic recrystallization </span><span>processes </span><span>given the local ice temperature, stress- and strain-rate states. We argue that the free parameters may be constrained by consulting the ice-core literature and present tentative simulations of the GRIP ice-core fabric.</span></p>

scholarly journals Forecasting the Intra-Day Spread Densities of Electricity Prices

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 687 ◽  
Author(s):  
Ekaterina Abramova ◽  
Derek Bunn
Keyword(s):  
Loss Function ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Density Functions ◽  
Shape Parameters ◽  
Electricity Prices ◽  
Dynamic Density ◽  
Cumulative Distribution Functions ◽  
Pinball Loss ◽  
Best Fitting

Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions.

scholarly journals Mathematical modeling on the base of functions density of normal distribution

2021 ◽  
Vol 12 (33) ◽  
pp. 34-49
Author(s):  
Iuliia Pinkovetskaia ◽  
Yulia Nuretdinova ◽  
Ildar Nuretdinov ◽  
Natalia Lipatova
Keyword(s):  
Normal Distribution ◽  
Empirical Data ◽  
Comprehensive Evaluation ◽  
Statistical Tests ◽  
Methodological Approach ◽  
Distribution Functions ◽  
Density Functions ◽  
Large Sets ◽  
Kolmogorov Smirnov

One of the urgent tasks in many modern scientific studies is the comparative analysis of indicators that characterize large sets of similar objects located in different regions. Given the significant differences between the regions compared, this analysis should be carried out using relative indicators. The objective of the study was to use the density functions of the normal distribution to model empirical data that describe the compared sets of objects located in different regions. The methodological approach was based on the Chebyshev and Lyapunov theorems. The research results focus on the main stages of the construction of normal distribution functions and the corresponding histograms, as well as the determination of the parameters of these functions. The work possesses a degree of originality, since it provides answers to questions such as the justification of the necessary information base; performing computational experiments and developing alternative options for the generation of normal distribution density functions; comprehensive evaluation of the quality of the functions obtained through three statistical tests: Pearson, Kolmogorov-Smirnov, Shapiro-Wilk; identification of patterns that characterize the distribution of indicators of the sets of objects considered. Examples of empirical data models are given using distribution functions to estimate the share of innovative firms in the total number of firms in the regions of Russia.

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