21 papers · ranked by Valyu relevance
Chengfeng Lei, Xiulian Sun
Background Evaluating the toxicity or effectiveness of two or more toxicants in a specific population often requires specialized statistical software to calculate and compare median lethal doses (LD50s). Tests for equality of LD50s using probit regression with parallel slopes have been implemented in many software…
Alexander Munteanu, Simon Omlor, C. H. F. Petèrs
We study the p-generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard normal cdf, by a p-generalized normal distribution for p ∈ [1, ∞). The p-generalized normal distributions (Subbotin, 1923) are…
Ang Li, Luis Pericchi, Kun Wang
There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using probit regression and logistic regression with normal priors. In this…
Niccolò Anceschi, Augusto Fasano, Daniele Durante, Giacomo Zanella
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical Gaussian response settings, this class also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding one of the…
Daniele Durante
Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve also as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference…
Janet van Niekerk, Håvard Rue
Skewed probit regression is but one example of a statistical model that generalizes a simpler model, like probit regression. All skew-symmetric distributions and link functions arise from symmetric distributions by incorporating a skewness parameter through some skewing mechanism. In this work we address some…
Francesca Ieva, Giampiero Marra, Anna Maria Paganoni, Rosalba Radice
In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical…
Patrick Ding, Guido W. Imbens, Zhaonan Qu, Yinyu Ye
Probit models are useful for modeling correlated discrete responses in many disciplines, including consumer choice data in economics and marketing. However, the Gaussian latent variable feature of probit models coupled with identification constraints pose significant computational challenges for its estimation and…
Javier Alejo, Gabriel Montes‐Rojas
A new Stata command, ldvqreg, is developed to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimators are implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it…
Dungang Liu, Xiaorui Zhu, Brandon Greenwell, Zewei Lin
Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square…
Udo Boehm, Helen Steingroever, Eric-Jan Wagenmakers
An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific…
Sadia Mahmud, WY Wendy Lou, Neil W Johnston
Background A zero-inflated continuous outcome is characterized by occurrence of "excess" zeros that more than a single distribution can explain, with the positive observations forming a skewed distribution. Mixture models are employed for regression analysis of zero-inflated data. Moreover, for repeated measures…
Simon Bang Kristensen, Kristian Sandberg, Bo Martin Bibby
Metacognition is an important component in basic science and clinical psychology, often studied through complex, cognitive experiments. While Signal Detection Theory (SDT) provides a popular and pervasive framework for modelling responses from such experiments, a shortfall remains that it cannot in a straightforward…
Patricia Pinamang Acheampong, Elvis Agyei Obeng, Monica Opoku, Lydia Brobbey + 1 more
Household food security exists when households have physical, social and economic access to sufficient, safe and nutritious food at all times that meets their dietary needs and food preferences for an active and healthy life. Food security remains a serious challenge for many households in Ghana and the situation is…
Shreeya Banerji
Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition…
John L. Moran, John D. Santamaria, Graeme J. Duke
Background Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry…
Essoham Ali, Kim-Hung Pho
- 1. This study proposes some new models for family Zero-inflated Bernoulli models. - 2. This is the first work to be carried out for this model. - 3. Maximum likelihood estimator is used to check its performance of models under consideration. - 4. Many simulations and a real data set are performed in this research.
Saikat Banerjee, Lingyao Zeng, Heribert Schunkert, Johannes Söding
Genetic variants in genome-wide association studies (GWAS) are tested for disease association mostly using simple regression, one variant at a time. Standard approaches to improve power in detecting disease-associated SNPs use multiple regression with Bayesian variable selection in which a sparsity-enforcing prior on…
Sahar Zarmehri, Ephraim M. Hanks, Lin Lin
The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially…
Satwik Acharyya, Debdeep Pati, Dipankar Bandyopadhyay, Shumei Sun
Beta distributions are commonly used to model proportion valued response variables, commonly encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…