Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Milena Rmus, Ti-Fen Pan, Liyu Xia, Anne G. E. Collins
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best…
Roberto Trotta
These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level starts from elementary notions, without assuming any previous knowledge of…
A. Ian McLeod, Benoît Quenneville
1A.I. McLeod is Professor, Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada, N6A 5B7, e-mail: aim@uwo.ca. B. Quenneville is Senior Methodologist, Time Series Research and Analysis Centre, Statistics Canada, Ottawa, Ontario, Canada, K1A 0T6. e-mail…
Akatsuki Kimura, Antonio Celani, Hiromichi Nagao, Timothy Stasevich + 1 more
'Kazuyuki Nakamura'] Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental…
Arwa M. Alshangiti, M. Kayid, B. Alarfaj
The purpose of this paper is to provide further study of the Marshall-Olkin log-logistic model that was first described by Gui (Appl Math Sci 7:3947-3961, [8]). This model is both useful and practical in areas such as reliability and life testing. Some statistical and reliability properties of this model are presented…
Antonio Calcagnì, Livio Finos, Gianmarco Altoé, Massimiliano Pastore
In this article, we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy (ME) approach. Unlike standard procedures that require equating the score function of the maximum likelihood problem at zero, we propose an alternative strategy where the score is instead…
Tiffany N. Kolba, Alexander Bruno, Eugene Demidenko
We examine the accuracy and precision of parameter estimates for both the exponential and normal distributions when using only a collection of sample extremes. That is, we consider a collection of random variables, where each of the random variables is either the minimum or maximum of a sample of n*j independent…
Pedro Luiz Ramos, Eduardo Ramos, Francisco A. Rodrigues, Francisco Louzada
'Francisco Louzada'] The maximum likelihood estimator plays a fundamental role in statistics. However, for many models, the estimators do not have closed-form expressions. This limitation can be significant in situations where estimates and predictions need to be computed in real-time, such as in applications based on…
Ludger Starke, Dirk Ostwald
Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation…
Mintodê Nicodème Atchadé, Melchior N’bouké, Aliou Moussa Djibril, Shabnam Shahzadi + 6 more
'Shabnam Shahzadi' 'Eslam Hussam' 'Ramy Aldallal' 'Huda M. Alshanbari' 'Ahmed M. Gemeay' 'Abdal-Aziz H. El-Bagoury' 'Anoop Kumar'] We introduced a brand-new member of the family that is going to be referred to as the New Power Topp-Leone Generated (NPTL-G). This new member is one of a kind. Given the major functions…
Antonio Calcagnì, Livio Finos, Gianmarco Altoè, Massimiliano Pastore
In this article we provide initial findings regarding the problem of solving likelihood equations by means of a maximum entropy approach. Unlike standard procedures that require equating at zero the score function of the maximum-likelihood problem, we propose an alternative strategy where the score is instead used as…
Authors not listed
Knowledge of the reaction rate constants can be vital in understanding electrochemical reaction mechanisms and their rate-determining processes. Although first-principles methods, such as density functional theory (DFT), provide valuable insight into reaction free energies and rate constants, they commonly use…
Ehsan Fayyazishishavan, Serpil Kılıç Depren, Feng Chen
The two-parameter of exponentiated Gumbel distribution is an important lifetime distribution in survival analysis. This paper investigates the estimation of the parameters of this distribution by using lower records values. The maximum likelihood estimator (MLE) procedure of the parameters is considered, and the Fisher…
Van N. T. La, Stanley Nicholson, Amna Haneef, Lulu Kang + 1 more
Some data are just underappreciated. Maybe they look different or come from a different background than most other data. Maybe they don't fit neatly into common notions of what data on a ``curve'' should look like. Whatever the case, they are pigeonholed into a restricted role that limits their contributions. But if…
Nils Lid Hjort
Protesting mildly against the notion of an exactly correct parametric model the view is adopted that the logistic regression equation is merely an approximation to the underlying, true function. The behaviour of likelihood based estimators is investigated in such a general framework. The maximum likelihood estimator is…
Grégory Nuel, Andréa Rau, Florence Jaffrézic
Methodological development for the inference of gene regulatory networks from transcriptomic data is an active and important research area. Several approaches have been proposed to infer relationships among genes from observational steady-state expression data alone, mainly based on the use of graphical Gaussian…
William Ruth
The EM algorithm is a powerful tool for maximum likelihood estimation with missing data. In practice, the calculations required for the EM algorithm are often intractable. We review numerous methods to circumvent this intractability, all of which are based on Monte Carlo simulation. We focus our attention on the Monte…
Neriman Akdam, Osama Abdulaziz Alamri, Subhankar Dutta, Fatma Çiftci + 1 more
In this study, the maximum likelihood estimators (MLEs) and Bayes estimators for the shape and scale parameters of Inverse Exponential Power (IEP) distribution are derived. As closed-form solutions for the Bayes estimators are not available, approximate estimators are obtained through Lindley’s and Tierney-Kadane’s…
Xiaolu Wang, Peter Dayan, Paul M Bays
The activity of neural populations typically encodes more information about sensory or motor variables than can be captured by point estimates of the variables. We present and compare two approaches to quantifying this additional or ancillary information and its relationship to uncertainty: the mutual information…
A. E. Allahverdyan
We study the parameter estimation problem in mixture models with observational nonidentifiability: the full model (also containing hidden variables) is identifiable, but the marginal (observed) model is not. Hence global maxima of the marginal likelihood are (infinitely) degenerate and predictions of the marginal…
E. G. Cooch, D. I. MacKenzie, J. A. Royle
Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Matthew J. Simpson, Oliver J. Maclaren
Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…