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About 10,000 papers for “distribution fitting”

Distribution Fitting.

Nicholas F. Marko (123844), Robert J. Weil (123848) · Figshare · 2015

<p>Distribution fitting for the brain cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian (normal) distribution. Similar graphs for additional tumor types are given in figures S2, S3, S4, S5.</p>

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Empiric Distribution Fitting.

Nicholas F. Marko (123844), Robert J. Weil (123848) · Figshare · 2015

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046935#s2" target="_blank">Results</a> of the empiric distribution fitting for each of the five cancer genomes normalized using each of two normalization methods. RMA = Robust Multichip Average. The top three distributions in each category appear in the order of their overall goodness of fit.</p>

Cited by 0Open access

Distribution fitting in C++

Esgueva Moro, Pablo · UPM Digital Archive (Technical University of Madrid) · 2025

Este trabajo tiene como objetivo el desarrollo de una herramienta en C++ que permita realizar ajustes de distribuciones estadísticas teóricas a conjuntos de datos numéricos. Esta aplicación proporciona una interfaz gráfica intuitiva que facilita al usuario realizar acciones como cargar los datos, aplicar pruebas de bondad de ajuste, calcular los estimadores de cada distribución y visualizar los resultados mediante histogramas y curvas de ajuste. El proyecto nace de la necesidad de crear una solución de carácter académico gratuita y de código abierto, combinando las mejores características de otras herramientas existentes como las desarrolladas en Python o R, o softwares comerciales como Stat::Fit o IBM SPSS Statistics, pero sin necesidad de conocimientos técnicos de programación ni el coste de licencias. La herramienta implementa una gran variedad de distribuciones (Normal, Exponencial, Gamma, Weibull entre otras) y algunas de las pruebas estadísticas más conocidas como Kolmogórov-Smirnov, Chi-Cuadrado, Anderson-Darling, etc. Respecto a la estructura del proyecto, sigue un esquema modular, con etapas claramente divididas como el análisis de requisitos, diseño, implementación y validación. Además, puesto que se trata de un proyecto de ingeniería del software, se utilizan metodologías propias de este. Una de las motivaciones principales es aplicar el software para analizar datos de estudios sobre los patrones de comportamiento de compradores en tiendas físicas, comparando los resultados con las investigaciones previamente realizadas. El sistema por otro lado permite la comparación entre distintas distribuciones, facilitando así la extracción de conclusiones de forma visual. Finalmente, el proyecto demuestra el valor práctico del software como herramienta para el análisis estadístico, permitiendo su uso en investigaciones reales. ABSTRACT This work aims to develop a tool in C++ that allows for the fitting of theorical statistical distributions to numerical data sets. This application provides an intuitive graphical interface that makes it easy for the user to perform actions such as loading data, applying goodness-of-fit tests, calculating the estimators for each distribution, and visualizing the results through histograms and fit curves. The project arises from the need to create a free and open-source academic solution, combining the best features of other existing tools such as those developed in Python or R, or commercial software like Stat::Fit or IBM SPSS Statistics, but without the need for technical programming knowledge or the cost of licenses. The tool implements a wide variety of distributions (Normal, Exponential, Gamma, Weibull among others) and some of the most well-known statistical tests such as Kolmogorov-Smirnov, Chi-Squared, Anderson-Darling, etc. Regarding the project structure, it follows a modular scheme, with clearly divided stages such as requirements analysis, design, implementation, and validation. Additionally, since it is a software engineering project, methodologies specific to this field are used. One of the main motivations is to apply the software to analyse data from studies on the shopping behavior patterns of customers in physical stores, comparing the results with previously conducted research. The system, on the other hand, allows for the comparison between different distributions, thereby facilitating the extraction of conclusions visually. Finally, the project demonstrates the practical value of the software as a tool for statistical analysis, allowing its use in real research.

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Fitting the degree distribution.

Valerie C. Valerio (8843435), Olivier J. Walther (8843438), Marjatta Eilittä (8843441) et al. · Figshare · 2020

Cited by 0Open access

dftools: Distribution function fitting

Danail Obreschkow · UWA Profiles and Research Repository (UWA) · 2018

dftools, written in R, finds the most likely P parameters of a D-dimensional distribution function (DF) generating N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects are galaxies, it can fit a mass function (D=1), a mass-size distribution (D=2) or the mass-spin-morphology distribution (D=3). Unlike most common fitting approaches, this method accurately accounts for measurement in uncertainties and complex selection functions.

Cited by 0

Fitting a Conditional Gaussian Distribution

Kevin P. Murphy · 1998

Introduction We consider the problem of nding the Maximum Likelihood (ML) estimates of the parameters of a conditional Gaussian node Y with continuous parent X and discrete parent Q, i.e., p(yjx; Q = i) = cj i j 1 2 exp 1 2 (y B i x) 0 1 i (y B i x) where c = (2) d=2 is a constant and jyj = d. The j'th row of B i is the regression vector for the j component of y given that Q = i. To allo

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distributionsrd: Distribution Fitting and Evaluation

Ruben Dewitte · 2020

A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) &lt;<a href="https://hdl.handle.net/1854/LU-8644700" target="_top">https://hdl.handle.net/1854/LU-8644700</a>&gt; for a description and application of methods available in this package.

Cited by 1Open access

Distribution fitting and parameter estimation.

Shuang Lin (2438698), Shengda Zhang (14805766), Chaofeng Wang (588272) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging. This paper proposes an innovative approach using the stable Paretian model to analyze the price return of jet fuel in large samples. It comprehensively compares the fitting effect of the stable Paretian distribution with that of the normal distribution based on specific criteria and non-parametric significance tests. Furthermore, it investigates the accuracy of risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) predicted by both models. In addition to comparing differences in VaR between predicted values and actual values, this paper provides a more comprehensive comparison of risk measures under rolling window forecast situation. Results suggest that despite indistinguishable results in VaR backtest, the stable Paretian distribution has a overall better fitting effect as well as a less biased predicted CVaR based on the AIC of -14099.46, BIC of -14110.98, &lt;i&gt;p&lt;/i&gt; = 0.58 in Kolmogorov-Smirnov test and &lt;i&gt;p&lt;/i&gt; = 0.46(0.92) in the 0.01(0.05) significance level of Expected Shortfall Regression Test. This might be explained by its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines’ risk management decisions. its ability to capture asset return dynamics while maintaining shape stability with few parameters. This research can provide valuable insights for guiding airlines’ risk management decisions.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Error distribution of calibration fitting.

Keying Guo (4955050), Yazhong Lu (22127355), Zhihui Liu (95891) · Figshare · 2025

<div> A thin-film thermocouple sensor directly deposited on nickel-based superalloys was investigated for aero-engine applications. To address the high-temperature and harsh environment challenges, the sensor’s film structure was meticulously designed. A NiCrAlY thin-film transition layer was deposited via magnetron sputtering, while an Al₂O₃ thin-film insulation layer and protective layer were prepared using electron beam evaporation and RF magnetron sputtering. Additionally, a Pt10Rh/Pt thin-film sensitive layer was fabricated via DC magnetron sputtering. Results indicate that the thin-film thermocouple sensor can operate at temperatures up to 1100 °C, with a Seebeck coefficient of 8.16 μV/°C, a maximum temperature error below ±1%, and a service life exceeding 20 hours. This sensor withstands the harsh conditions of aero engines, significantly improves local temperature measurement accuracy, and offers valuable insights for turbine blade lifespan and cooling design. </div>

Cited by 0Open access

Fitting the origin-centered Hamming distribution.

Bjarke Frost Nielsen (8571193), Chadi M. Saad-Roy (9027875), Yimei Li (778105) et al. · Figshare · 2023

Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.

Cited by 0Open access

Frequency Distribution Fitting for Electronic Documents

Arockia David Roy Kulandai · International Journal of Applied Sciences and Smart Technologies · 2021

Studies of frequency distributions of natural language elements have identified some distributions that offer a good fit. Using electronic documents, we show that some of these distributions cannot be used to model the frequency of bytes in electronic documents even if these documents represent natural language documents.

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Fitting the Skellam distribution in Stata

Vincenzo Verardi · RePEc: Research Papers in Economics

The Skellam distribution is a discrete probability distribution related to the difference between two independent Poisson-distributed random variables. It has been used in a variety of contexts, including sports or supply and demand imbalances in shared transportation. To the best of our knowledge, Stata does not support the Skellam distribution or the Skellam regression. In this talk we plan to show how to fit the parameters of a Skellam distribution and Skellam regression using Mata’s optimize function. The optimization problem is then packaged into a basic Stata command that we plan to describe.

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