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At least 919 records · Page 51Linked to original sources
arXiv2023

Evaluation of Active Affiliates to the SIS Multidimensional Analysis in R Shiny

This article presents a study that uses multiple linear regression analysis to examine the factors influencing the number of people affiliated with different insurance plans within the Comprehensive Health Insurance (SIS) system in Peru.The study highlights the importance of multiple linear regression analysis in understanding the factors that affect SIS Comprehensive Health Insurance affiliates. It also showcases the value of utilizing interactive tools like RShiny to enhance data analysis, providing a dynamic and participatory experience for researchers and users interested in the subject.To facilitate the analysis and visualization of SIS-related data, the researchers developed an interactive application using RShiny. This tool allows for the easy loading, visualization, and analysis of data in a user-friendly and practical manner. By providing an interactive platform, users can effectively explore and understand the factors that impact SIS affiliates.The results of the analysis indicate that the selected variables have a significant positive influence on the total number of affiliates. This suggests that the specific insurance plan examined in this study has a favorable effect on the enrollment of individuals in SIS. Additionally, the data shows a linear trend, supporting the use of a linear regression model to describe this relationship. Active affiliates,Comprehensive health insurance SIS,Data Visualization,Multiple Linear Regression Analysis,RShiny

stat.OT↗
arXiv2023

Prediction of breast cancer with 98% accuracy

Abstract Cancer is a tumor that affects people worldwide, with a higher incidence in females but not excluding males. It ranks among the top five deadliest types of cancer, particularly prevalent in less developed countries with deficient healthcare programs. Finding the best algorithm for effective breast cancer prediction with minimal error is crucial. In this scientific article, we employed the SMOTE method in conjunction with the R package Shiny to enhance the algorithms and improve prediction accuracy. We classified the tumor types as benign and malignant (B/M). Various algorithms were analyzed using a Kaggle dataset, and our study identified the superior algorithm as logistic regression. We evaluated algorithm performance using confusion matrices to visualize results and the ROC Curve to obtain a comprehensive measure of performance. Additionally, we calculated precision by dividing the number of correct predictions by the total predictions Keywords Breast cancer, Smote, Benign, Malignant.

stat.OT↗
arXiv2023

J. B. S. Haldane's Rule of Succession

After Bayes, the oldest Bayesian account of enumerative induction is given by Laplace's so-called rule of succession: if all $n$ observed instances of a phenomenon to date exhibit a given character, the probability that the next instance of that phenomenon will also exhibit the character is $\frac{n+1}{n+2}$. Laplace's rule however has the apparently counterintuitive mathematical consequence that the corresponding "universal generalization" (every future observation of this type will also exhibit that character) has zero probability. In 1932, the British scientist J. B. S. Haldane proposed an alternative rule giving a universal generalization the positive probability $\frac{n+1}{n+2} \times \frac{n+3}{n+2}$. A year later Harold Jeffreys proposed essentially the same rule in the case of a finite population. A related variant rule results in a predictive probability of $\frac{n+1}{n+2} \times \frac{n+4}{n+3}$. These arguably elegant adjustments of the original Laplacean form have the advantage that they give predictions better aligned with intuition and common sense. In this paper we discuss J. B. S. Haldane's rule and its variants, placing them in their historical context, and relating them to subsequent philosophical discussions.

stat.OT↗
arXiv2023

DataXploreFines: Generalized Data for Informed Decision, Making, An Interactive Shiny Application for Data Analysis and Visualization

This article presents DataXploreFines, an innovative Shiny application that revolutionizes data exploration, analysis, and visualization. The application offers functionalities for data loading, management, summarization, basic graphs, advanced analysis, and contact. Users can upload their datasets in popular formats like CSV or Excel, explore the data structure, perform manipulations, and obtain statistical summaries. DataXploreFines provides a wide range of interactive visualizations, including histograms, scatter plots, bar charts, and line graphs, enabling users to identify patterns and trends. Additionally, the application offers statistical tools such as time series analysis using ARIMA and SARIMA models, forecasting, and Ljung-Box statistic. Its user-friendly interface empowers individuals from various domains, including beginners in statistics, to make informed decisions.

stat.OT↗
arXiv2023

Edgewise outliers of network indexed signals

We consider models for network indexed multivariate data involving a dependence between variables as well as across graph nodes. In the framework of these models, we focus on outliers detection and introduce the concept of edgewise outliers. For this purpose, we first derive the distribution of some sums of squares, in particular squared Mahalanobis distances that can be used to fix detection rules and thresholds for outlier detection. We then propose a robust version of the deterministic MCD algorithm that we call edgewise MCD. An application on simulated data shows the interest of taking the dependence structure into account. We also illustrate the utility of the proposed method with a real data set.

stat.ME↗
arXiv2023

Comparative analysis using classification methods versus early stage diabetes

In this research work, a comparative analysis was carried out using classification methods such as: Discriminant Analysis and Logistic Regression to subsequently predict whether a person may have the presence of early stage diabetes. For this purpose, use was made of a database of the UC IRVINE platform of the year 2020 where specific variables that influence diabetes were used for a better result. Likewise in terms of methodology, the corresponding analysis was performed for each of the 3 classification methods and then take them to a comparative table and analyze the results obtained. Finally we can add that the majority of the studies carried out applying the classification methods to the diseases can be clearly seen that there is a certain attachment and more use of the logistic regression classification method, on the other hand, in the results we could see significant differences in terms of the 2 classification methods that were applied, which was valuable information for later drawing final conclusions.

stat.OT↗
arXiv2023

CATASTROAGRI -- Interactive data analysis and visualization application with a future projection for catastrophic agricultural insurance

CATASTROAGRI is an application developed to load, analyze and interactively visualize relevant data on catastrophic agricultural insurance. It also focuses on the analysis of an ARIMA (0,1,1) (0,1,1) model to identify and estimate patterns in the agricultural data of the Puno Region, it presents a decreasing trend because there is a significant relationship between successive values of the time series, We can also state that it is not stationary because the mean and variance do not remain constant over time and the series has periods, and it is observed that the cases are decreasing and increasing over the years, especially the amount to indemnify due to the behavior of the climate in the highlands. The results of the analysis show that agricultural insurance plays an important role in protecting farmers against losses caused by adverse climatic events. The importance of concentrating resources and indemnities on the most affected crops and in the provinces with the highest agricultural production is emphasized. The results of the users' evaluation showed a high level of satisfaction, as well as ease of use.

stat.OT↗
arXiv2023

Time Series Analysis Applied to Notifications of Work Accidents

Time series analysis applied to occupational accident reports is a powerful tool for understanding the evolution of occupational accidents over time. It provides valuable information to make informed decisions. In this study, data from reports of work accidents collected from the MINISTRY OF LABOR AND EMPLOYMENT PROMOTION. MTPE were analyzed by time series. Significant patterns and trends in accident reporting have been identified, leading to more effective prevention strategies and better health and safety management.

stat.OT↗
arXiv2023

Prediction of depression status in college students using a Naive Bayes classifier based machine learning model

This study presents a machine learning model based on the Naive Bayes classifier for predicting the level of depression in university students, the objective was to improve prediction accuracy using a machine learning model involving 70% training data and 30% validation data based on the Naive Bayes classifier, the collected data includes factors associated with depression from 519 university students, the results showed an accuracy of 78.03%, high sensitivity in detecting positive cases of depression, especially at moderate and severe levels, and significant specificity in correctly classifying negative cases, these findings highlight the effectiveness of the model in early detection and treatment of depression, benefiting vulnerable sectors and contributing to the improvement of mental health in the student population.

stat.OT↗
arXiv2023

Binary classification based Monte Carlo simulation

Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve computing ratios of probability density functions (pdfs). On the other hand, classifiers discriminate labeled samples produced by a mixture of two distributions and can be used for approximating the ratio of the two corresponding pdfs.This bridge between simulation and classification enables us to propose pdf-free versions of pdf-ratio-based simulation algorithms, where the ratio is replaced by a surrogate function computed via a classifier. From a probabilistic modeling perspective, our procedure involves a structured energy based model which can easily be trained and is compatible with the classical samplers.

stat.ME↗
arXiv2023

A One-Parameter Diagnostic Classification Model with Familiar Measurement Properties

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent characteristics. These models are well-suited for providing diagnostic and actionable feedback to support formative assessment efforts. Several DCMs have been developed and applied in different settings. This study proposes a DCM with functional form similar to the 1-parameter logistic item response theory model. Using data from a large-scale mathematics education research study, we demonstrate that the proposed DCM has measurement properties akin to the Rasch and 1-parameter logistic item response theory models, including test score sufficiency, item-free and person-free measurement, and invariant item and person ordering. We discuss the implications and limitations of these developments, as well as directions for future research.

stat.AP↗
arXiv2023

The Mastery Rubric for Statistics and Data Science: promoting coherence and consistency in data science education and training

Consensus based publications of both competencies and undergraduate curriculum guidance documents targeting data science instruction for higher education have recently been published. Recommendations for curriculum features from diverse sources may not result in consistent training across programs. A Mastery Rubric was developed that prioritizes the promotion and documentation of formal growth as well as the development of independence needed for the 13 requisite knowledge, skills, and abilities for professional practice in statistics and data science, SDS. The Mastery Rubric, MR, driven curriculum can emphasize computation, statistics, or a third discipline in which the other would be deployed or, all three can be featured. The MR SDS supports each of these program structures while promoting consistency with international, consensus based, curricular recommendations for statistics and data science, and allows 'statistics', 'data science', and 'statistics and data science' curricula to consistently educate students with a focus on increasing learners independence. The Mastery Rubric construct integrates findings from the learning sciences, cognitive and educational psychology, to support teachers and students through the learning enterprise. The MR SDS will support higher education as well as the interests of business, government, and academic work force development, bringing a consistent framework to address challenges that exist for a domain that is claimed to be both an independent discipline and part of other disciplines, including computer science, engineering, and statistics. The MR-SDS can be used for development or revision of an evaluable curriculum that will reliably support the preparation of early e.g., undergraduate degree programs, middle e.g., upskilling and training programs, and late e.g., doctoral level training practitioners.

stat.OT↗
arXiv2023

The Lindy Effect

The Lindy effect is a statistical tendency for things with longer pasts behind them to have longer futures ahead. It has been experimentally confirmed to apply to some categories, but not others, raising questions about when it is applicable and why. I shed some light on these questions by examining the mathematical properties required for the effect and generating mechanisms that can produce them. While the Lindy effect is often thought to require a declining hazard rate, I show that it arises very naturally even in cases with constant (or increasing) hazard rates -- so long as there is a probability distribution over the size of that rate. One implication is that even things which are becoming less robust over time can display the Lindy effect.

physics.soc-ph↗
arXiv2023

Spatial and Spatiotemporal Volatility Models: A Review

Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, if two locations are in close proximity, they can exhibit similar volatilities. In this paper, we aim to provide a comprehensive review of the recent literature on spatial and spatiotemporal volatility models. We first briefly review time series volatility models and their multivariate extensions to motivate their spatial and spatiotemporal counterparts. We then review various spatial and spatiotemporal volatility specifications proposed in the literature along with their underlying motivations and estimation strategies. Through this analysis, we effectively compare all models and provide practical recommendations for their appropriate usage. We highlight possible extensions and conclude by outlining directions for future research.

econ.EM↗
arXiv2023

A generalized Bayesian stochastic block model for microbiome community detection

Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated the microbiome study. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To this end, we propose a Bayesian stochastic block model to study the microbiome co-occurrence network based on the recently developed modified centered-log ratio transformation tailored for microbiome data analysis. Our model allows us to incorporate taxonomic tree information using a Markov random field prior. The model parameters are jointly inferred by using Markov chain Monte Carlo sampling techniques. Our simulation study showed that the proposed approach performs better than competing methods even when taxonomic tree information is non-informative. We applied our approach to a real urinary microbiome dataset from postmenopausal women, the first time the urinary microbiome co-occurrence network structure has been studied. In summary, this statistical methodology provides a new tool for facilitating advanced microbiome studies.

stat.ME↗
arXiv2023

Bayesian questions with frequentist answers

The two statistical methods, namely the frequentist and the Bayesian methods, are both commonly used for probabilistic inference in many scientific situations. However, it is not straightforward to interpret the result of one approach in terms of the concepts of the other. In this paper we explore the possibility of finding a Bayesian significance for the frequentist's main object of interest, the $p$-value, which is the probability assigned to the proposition -- which we call the {\it extremity proposition} -- that a measurement will result in a value that is at least as extreme as the value that was actually obtained. To make contact with the frequentist language, the Bayesian can choose to update probabilities based on the {\it extremity proposition}, which is weaker than the standard Bayesian update proposition, which uses the actual observed value. We then show that the posterior probability (or probability density) of a theory is equal to the prior probability (or probability density) multiplied by the ratio of the $p$-value for the data obtained, given that theory, to the mean $p$-value -- averaged over all theories weighted by their prior probabilities. Thus, we provide frequentist answers to Bayesian questions. Our result is generic -- it does not rely on restrictive assumptions about the situation under consideration or specific properties of the likelihoods or the priors.

physics.data-an↗
arXiv2023

Logic of subjective probability

In this paper I discuss both syntax and semantics of subjective probability. The semantics determines ways of testing probability statements. Among important varieties of subjective probabilities are intersubjective probabilities and impersonal probabilities, and I will argue that well-tested impersonal probabilities acquire features of objective probabilities. Jeffreys's law, my next topic, states that two successful probability forecasters must issue forecasts that are close to each other, thus supporting the idea of objective probabilities. Finally, I will discuss connections between subjective and frequentist probability.

cs.AI↗