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At least 793 records · Page 44Linked to original sources

arXiv2022

Non-iterative Gaussianization

In this work, we propose a non-iterative Gaussian transformation strategy based on copula function, which doesn't require some commonly seen restrictive assumptions in the previous studies such as the elliptically symmetric distribution assumption and the linear independent component analysis assumption. Theoretical properties guarantee the proposed strategy can exactly transfer any random variable vector with a continuous multivariate distribution to a variable vector that follows a multivariate Gaussian distribution. Simulation studies also demonstrate the outperformance of such a strategy compared to some other methods like Box-Cox Gaussianization and radial Gaussianization. An application for probability density estimation for image synthesis is also shown.

stat.ME↗

arXiv2022

Length L-function for Network-Constrained Point Data

Network constrained points are referred to as points restricted to road networks, such as taxi pick up and drop off locations. A significant pattern of network constrained points is referred to as an aggregation; e.g., the aggregation of pick up points may indicate a high taxi demand in a particular area. Although the network K function using the shortest path network distance has been proposed to detect point aggregation, its statistical unit is still radius based. R neighborhood, in particular, has inconsistent network length owing to the complex configuration of road networks which cause unfair counts and identification errors in networks (e.g., the length of the r neighborhood located at an intersection is longer than that on straight roads, which may include more points). In this study, we derived the length L function for network constrained points to identify the aggregation by designing a novel neighborhood as the statistical unit; the total length of this is consistent throughout the network. Compared to the network K function, our method can detect a true to life aggregation scale, identify the aggregation with higher network density, as well as identify the aggregations that the network K function cannot. We validated our method using taxi trips pick up location data within Zhongguancun Area in Beijing, analyzing differences in maximal aggregation between workdays and weekends to understand taxi demand in the morning and evening peak.

stat.OT↗

arXiv2022

Causal inference: critical developments, past and future

Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of `fairness' in comparisons dates back several hundred years, and yet statistical concepts and developments that form the area of causal inference are only decades old. In this paper, we review core tenets and methods of causal inference and key developments in the history of the field. We highlight connections with traditional `associational' statistical methods, including estimating equations and semiparametric theory, and point to current topics of active research in this crucial area of our field.

stat.OT↗

arXiv2022

The loss value of multilinear regression

Determinant formulas are presented for: a certain positive semidefinite, hermitian matrix; the loss value of multilinear regression; the multiple linear regression coefficient.

stat.OT↗

arXiv2022

Six Statistical Senses

This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a "sense" because we think of these as essential in helping every statistical mind connect in constructive and insightful ways with statistical theory, methodologies, and computation, toward the ultimate goal of building statistical phronesis. The illustration of each sense with statistical principles and methods provides a sensical tour of the conceptual landscape of statistics, as a leading discipline in the data science ecosystem.

stat.OT↗

arXiv2022

Computational Statistics and Data Science in the Twenty-first Century

Data science has arrived, and computational statistics is its engine. As the scale and complexity of scientific and industrial data grow, the discipline of computational statistics assumes an increasingly central role among the statistical sciences. An explosion in the range of real-world applications means the development of more and more specialized computational methods, but five Core Challenges remain. We provide a high-level introduction to computational statistics by focusing on its central challenges, present recent model-specific advances and preach the ever-increasing role of non-sequential computational paradigms such as multi-core, many-core and quantum computing. Data science is bringing major changes to computational statistics, and these changes will shape the trajectory of the discipline in the 21st century.

stat.OT↗

arXiv2022

Comment on "The statistics wars and intellectual conflicts of interest" by D. Mayo

While P-values are widely abused, they are a useful tool for many purposes; banning them is analogous to banning scalpels because most people do not know how to perform surgery. Many reported P-values are not genuine P-values, for a variety of reasons. Perhaps the most widespread and pernicious problem is the Type III error of testing a statistical hypothesis that has little or no connection to the scientific hypothesis.

stat.OT↗

arXiv2022

Minimizing Fleet Size and Improving Bike Allocation of Bike Sharing under Future Uncertainty

As a rapidly expanding service, bike sharing is facing severe problems of bike over-supply and demand fluctuation in many Chinese cities. This study develops a large-scale method to determine the minimum fleet size under uncertainty, based on the bike sharing data of millions of trips in Nanjing. It is found that the algorithm of minimizing fleet size under the incomplete-information scenario is effective in handling future uncertainty. For a dockless bike sharing system, supplying 14.5% of the original fleet could meet 96.8% of trip demands. Meanwhile, the results suggest that providing a integrated service platform that integrates multiple companies can significantly reduce the total fleet size by 44.6%. Moreover, in view of the COVID-19 pandemic, this study proposes a social distancing policy that maintains a suitable usage interval. These findings provide useful insights for improving the resource efficiency and operational service of bike sharing and shared mobility.

stat.OT↗

arXiv2022

Wrapped Distributions on homogeneous Riemannian manifolds

We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries. Control over distributions' properties, such as parameters, symmetry and modality yield a family of flexible distributions that are straightforward to sample from, suitable for use within Monte Carlo algorithms and latent variable models, such as autoencoders. As an illustration, we empirically validate our approach by utilizing our proposed distributions within a variational autoencoder and a latent space network model. Finally, we take advantage of the generalized description of this framework to posit questions for future work.

math.ST↗

arXiv2022

Physical, subjective and analogical probability

The aim of this paper is to show that the concept of probability is best understood by dividing this concept into two different types of probability, namely physical probability and analogical probability. Loosely speaking, a physical probability is a probability that applies to the outcomes of an experiment that have been judged as being equally likely on the basis of physical symmetry. Physical probabilities are arguably in some sense 'objective' and possess all the standard properties of the concept of probability. On the other hand, an analogical probability is defined by making an analogy between the uncertainty surrounding an event of interest and the uncertainty surrounding an event that has a physical probability. Analogical probabilities are undeniably subjective probabilities and are not obliged to have all the standard mathematical properties possessed by physical probabilities, e.g. they may not have the property of additivity or obey the standard definition of conditional probability. Nevertheless, analogical probabilities have extra properties, which are not possessed by physical probabilities, that assist in their direct elicitation, general derivation, comparison and justification. More specifically, these properties facilitate the application of analogical probability to real-world problems that can not be adequately resolved by using only physical probability, e.g. probabilistic inference about hypotheses on the basis of observed data. Careful definitions are given of the concepts that are introduced and, where appropriate, examples of the application of these concepts are presented for additional clarity.

stat.OT↗

arXiv2022

Research on spatial information transmission efficiency and capability of safe evacuation signs

As an indispensable spatial direction information indicator for emergency evacuation, the spatial relationship between safety evacuation signs and evacuees will affect the response time of evacuees and the evacuation efficiency. This paper takes 2 kinds of common safety evacuation signs, hangtag-type and embedded, as the research object and designs space direction information transmission efficiency and capability simulation experiment and fire drill, the efficiency and capability of spatial direction information transmission of safety evacuation signs are studied. The results show that the space angle of the hangtag-type safety evacuation sign is inversely proportional to the information transmission efficiency and capability of the space direction, and the fire drill also confirms this conclusion. When the spatial angle of the embedded safety evacuation sign is 5°, the spatial direction information transmission efficiency and capability increase. Simultaneously, the average escape time of the participants in the fire drill was lower, and the percentage of choosing unfamiliarity exports increased. The evolution of spatial angle has no significant effect on the intention of the response of subjects of different genders; when choosing the direction, males are more easily affected by the change of spatial angle than females; the confidence level of females' choice is more easily affected by spatial angle. In addition, according to the research results, the corresponding three-dimensional structure safety evacuation signs are designed. The functional structure of the safety evacuation signs is perfected, which can effectively improve the efficiency of fire emergency evacuation.

stat.OT↗

arXiv2022

A Simulation-Optimization Framework To Improve The Organ Transplantation Offering System

We propose a simulation-optimization-based methodology to improve the way that organ transplant offers are made to potential recipients. Our policy can be applied to all types of organs, is implemented starting at the local level, is flexible with respect to simultaneous offers of an organ to multiple patients, and takes into account the quality of the organs under consideration. We describe in detail our simulation-optimization procedure and how it uses underlying real-world transplant data to inform the decision-making process. We present results using our liver and kidney models, where we show that, under our policy recommendations, more organs are utilized and the required times to allocate the organs are reduced -- sometimes dramatically.

physics.med-ph↗

arXiv2022

Bayesian estimation of in-game home team win probability for college basketball

Two new Bayesian methods for estimating and predicting in-game home team win probabilities are proposed. The first method has a prior that adjusts as a function of lead differential and time elapsed. The second is an adjusted version of the first, where the adjustment is a linear combination of the Bayesian estimator with a time-weighted pre-game win probability. The proposed methods are compared to existing methods, showing the new methods perform better for both estimation and prediction. The utility is illustrated via an application to the 2016 NCAA Division 1 Championship game.

stat.ME↗

arXiv2022

High-Frequency-Based Volatility Model with Network Structure

This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.

q-fin.ST↗

arXiv2023

Controlling for Latent Confounding with Triple Proxies

We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who tackle the problem of general measurement error. We call this the `triple proxy' approach because it requires three proxies that are jointly independent conditional on unobservables. We consider three different choices for the third proxy: it may be an outcome, a vector of treatments, or a collection of auxiliary variables. We compare to an alternative identification strategy introduced by \citet{Miao2018a} in which causal effects are identified using two conditionally independent proxies. We refer to this as the `double proxy' approach. The triple proxy approach identifies objects that are not identified by the double proxy approach, including some that capture the variation in average treatment effects between strata of the unobservables. Moreover, the conditional independence assumptions in the double and triple proxy approaches are non-nested.

econ.EM↗

arXiv2022

Complementary Goodness of Fit Procedure for Crash Frequency Models

This paper presents a new procedure for evaluating the goodness of fit of Generalized Linear Models (GLM) estimated with Roadway Departure (RwD) crash frequency data for the State of Hawaii on two-lane two-way (TLTW) state roads. The procedure is analyzed using ten years of RwD crash data (including all severity levels) and roadway characteristics (e.g., traffic, geometry, and inventory databases) that can be aggregated at the section level. The three estimation methods evaluated using the proposed procedure include: Negative Binomial (NB), Zero-Inflated Negative Binomial (ZINB), and Generalized Linear Mixed Model-Negative Binomial (GLMM-NB). The procedure shows that the three methodologies can provide very good fits in terms of the distributions of crashes within narrow ranges of the predicted mean frequency of crashes and in terms of observed vs. predicted average crash frequencies for those data segments. The proposed procedure complements other statistics such as Akaike Information Criterion, Bayesian Information Criterion, and Log-likelihood used for model selection. It is consistent with those statistics for models without random effects, but it diverges for GLMM-NB models. The procedure can aid model selection by providing a clear visualization of the fit of crash frequency models and allowing the computation of a pseudo R2 similar the one used in linear regression. It is recommended to evaluate its use for evaluating the trade-off between the number of random effects in GLMM-NB models and their goodness of fit using more appropriate datasets that do not lead to convergence problems.

stat.AP↗

arXiv2024

Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence

Pearson's r, the most widely-used correlation coefficient, is traditionally regarded as exclusively capturing linear dependence, leading to its discouragement in contexts involving nonlinear relationships. However, recent research challenges this notion, suggesting that Pearson's r should not be ruled out a priori for measuring nonlinear monotone relationships. Pearson's r is essentially a scaled covariance, rooted in the renowned Cauchy-Schwarz Inequality. Our findings reveal that different scaling bounds yield coefficients with different capture ranges, and interestingly, tighter bounds actually expand these ranges. We derive a tighter inequality than Cauchy-Schwarz Inequality, leverage it to refine Pearson's r, and propose a new correlation coefficient, i.e., rearrangement correlation. This coefficient is able to capture arbitrary monotone relationships, both linear and nonlinear ones. It reverts to Pearson's r in linear scenarios. Simulation experiments and real-life investigations show that the rearrangement correlation is more accurate in measuring nonlinear monotone dependence than the three classical correlation coefficients, and other recently proposed dependence measures.

stat.OT↗

arXiv2022

Examining the role of context in statistical literacy outcomes using an isomorphic assessment instrument

The central role of statistical literacy has been discussed extensively, emphasizing its importance as a learning outcome and in promoting a citizenry capable of interacting with the world in an informed and critical manner. Our work contributes to the growing literature on assessing and improving people's statistical literacy vis-a-vis contexts important in their professional and personal lives. We consider the measurement of contextualized statistics literacy - statistical literacy as applied to relevant contexts. We discuss the development of an isomorphic instrument modifying an existing assessment, design of a pilot study, and results which conclude that 1) the isomorphic assessment has comparable psychometric properties, and 2) test takers have lower statistical literacy scores on an assessment that incorporates relevant contexts.

stat.OT↗