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At least 595 records · Page 33Linked to original sources
arXiv2020

Comparison of Clinical Episode Outcomes between Bundled Payments for Care Improvement (BPCI) Initiative Participants and Non-Participants

Objective: To evaluate differences in major outcomes between Bundled Payments for Care Improvement (BPCI) participating providers and non-participating providers for both Major Joint Replacement of the Lower Extremity (MJRLE) and Acute Myocardial Infarction (AMI) episodes. Methods: A difference-in-differences approach estimated the differential change in outcomes for Medicare beneficiaries who had an MJRLE or AMI at a BPCI participating hospital between the baseline (January 2011 through September 2013) and intervention (October 2013 through December 2016) periods and beneficiaries with the same episode (MJRLE or AMI) at a matched comparison hospital. Main Outcomes and Measures: Medicare payments, LOS, and readmissions during the episode, which includes the anchor hospitalization and the 90-day post discharge period. Results: Mean total Medicare payments for an MJRLE episode and the 90-day post discharge period declined $444 more (p < 0.0001) for Medicare beneficiaries with episodes initiated in a BPCI-participating provider than for the beneficiaries in a comparison provider. This reduction was mainly due to reduced institutional post-acute care (PAC) payments. Slight reductions in carrier payments and LOS were estimated. Readmission rates were not statistically different between the BPCI and the comparison populations. These findings suggest that PAC use can be reduced without adverse effects on recovery from MJRLE. The lack of statistically significant differences in effects for AMI could be explained by a smaller sample size or more heterogenous recovery paths in AMI. Conclusions: Our findings suggest that, as currently designed, bundled payments can be effective in reducing payments for MJRLE episodes of care, but not necessarily for AMI. Most savings came from the declines in PAC. These findings are consistent with the results reported in the BPCI model evaluation for CMS.

stat.OT↗
arXiv2021

Hierarchical Bayesian propulsion power models for marine vessels

Assessing the magnitude of fuel consumption of marine traffic is a challenging task. The consumption can be reduced by the ways the vessels are operated, to achieve both improved cost efficiency and reduced CO2 emissions. Mathematical models for predicting ships' consumption are in a central role in both of these tasks. Nowadays, many ships are equipped with data collection systems, which enable data-based calibration of the consumption models. Typically this calibration procedure is carried out independently for each particular ship, using only data collected from the ship in question. In this paper, we demonstrate a hierarchical Bayesian modeling approach, where we fit a single model over many vessels, with the assumption that the parameters of vessels of same type and similar characteristics (e.g. vessel size) are likely close to each other. The benefits of such an approach are two-fold; 1) we can borrow information about parameters that are not well informed by the vessel-specific data using data from similar ships, and 2) we can use the final hierarchical model to predict the behavior of a vessel from which we don't have any data, based only on its characteristics. In this paper, we discuss the basic concept and present a first simple version of the model. We apply the Stan statistical modeling tool for the model fitting and use real data from 64 cruise ships collected via the widely used commercial Eniram platform. By using Bayesian statistical methods we obtain uncertainties for the model predictions, too. The prediction accuracy of the model is compared to an existing data-free modeling approach.

stat.AP↗
arXiv2020

Selective Confidence Intervals for Martingale Regression Model

In this paper we consider the problem of constructing confidence intervals for coefficients of martingale regression models (in particular, time series models) after variable selection. Although constructing confidence intervals are common practice in statistical analysis, it is challenging in our framework due to the data-dependence of the selected model and the correlation among the variables being selected and not selected. We first introduce estimators for the selected coefficients and show that it is consistent under martingale regression model, in which the observations can be dependent and the errors can be heteroskedastic. Then we use the estimators together with a resampling approach to construct confidence intervals. Our simulation results show that our approach outperforms other existing approaches in various data structures.

math.ST↗
arXiv2020

On the Bound of Cumulative Return in Trading Series and the Verification Using Technical Trading Rules

Although there is a wide use of technical trading rules in stock markets, the profitability of them still remains controversial. This paper first presents and proves the upper bound of cumulative return, and then introduces many of conventional technical trading rules. Furthermore, with the help of bootstrap methodology, we investigate the profitability of technical trading rules on different international stock markets, including developed markets and emerging markets. At last, the results show that the technical trading rules are hard to beat the market, and even less profitable than the random trading strategy.

q-fin.ST↗
arXiv2020

Linear Regression under Special Relativity

This study investigated the problem posed by using ordinary least squares (OLS) to estimate parameters of simple linear regression under a specific context of special relativity, where an independent variable is restricted to an open interval, (-c, c). It is found that the OLS estimate for the slope coefficient is not invariant under Lorentz velocity transformation. Accordingly, an alternative estimator for the parameters of linear regression under special relativity is proposed. This estimator can be considered a generalization of the OLS estimator under special relativity; when c approaches to infinity, the proposed estimator and its variance converges to the OLS estimator and its variance, respectively. The variance of the proposed estimator is larger than that of the OLS estimator, which implies that hypothesis testing using the OLS estimator and its variance may result in a liberal test under special relativity.

stat.OT↗
arXiv2020

Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective

Many research domains use data elicited from "citizen scientists" when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants' abilities. The model is described in the context of an ecological application that involves crowdsourced classifications of georeferenced coral-reef images from the Great Barrier Reef, Australia. The latent variable of interest is the proportion of coral cover, which is a common indicator of coral reef health. The participants' abilities are expressed in terms of sensitivity and specificity of a correctly classified set of points on the images. The model also incorporates a spatial component, which allows prediction of the latent variable in locations that have not been surveyed. We show that the model outperforms traditional weighted-regression approaches used to account for uncertainty in citizen science data. Our approach produces more accurate regression coefficients and provides a better characterization of the latent process of interest. This new method is implemented in the probabilistic programming language Stan and can be applied to a wide number of problems that rely on uncertain citizen science data.

stat.AP↗
arXiv2020

A Fair, Traceable, Auditable and Participatory Randomization Tool for Legal Systems

Many real-world scenarios require the random selection of one or more individuals from a pool of eligible candidates. One example of especial social relevance refers to the legal system, in which the jurors and judges are commonly picked according to some probability distribution aiming to avoid biased decisions. In this scenario, ensuring auditability of the random drawing procedure is imperative to promote confidence in its fairness. With this goal in mind, this article describes a protocol for random drawings specially designed for use in legal systems. The proposed design combines the following properties: security by design, ensuring the fairness of the random draw as long as at least one participant behaves honestly; auditability by any interested party, even those having no technical background, using only public information; and statistical robustness, supporting drawings where candidates may have distinct probability distributions. Moreover, it is capable of inviting and engaging as participating stakeholders the main interested parties of a legal process, in a way that promotes process transparency, public trust and institutional resilience. An open-source implementation is also provided as supplementary material.

cs.CR↗
arXiv2020

Extracting Spatiotemporal Demand for Public Transit from Mobility Data

With people constantly migrating to different urban areas, our mobility needs for work, services and leisure are transforming rapidly. The changing urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations or modelling that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variegated demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of approximately 4.6 million daily transit traces from the Greater London region reveals distinct demand profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our method of analysing the spatiotemporal geography of a city can be extended to other urban regions with different modes of public transit.

physics.soc-ph↗
arXiv2020

MMSE Bounds Under Kullback-Leibler Divergence Constraints on the Joint Input-Output Distribution

This paper proposes a new family of lower and upper bounds on the minimum mean squared error (MMSE). The key idea is to minimize/maximize the MMSE subject to the constraint that the joint distribution of the input-output statistics lies in a Kullback-Leibler divergence ball centered at some Gaussian reference distribution. Both bounds are tight and are attained by Gaussian distributions whose mean is identical to that of the reference distribution and whose covariance matrix is determined by a scalar parameter that can be obtained by finding the root of a monotonic function. The upper bound corresponds to a minimax optimal estimator and provides performance guarantees under distributional uncertainty. The lower bound provides an alternative to well-known inequalities in estimation theory, such as the Cramér-Rao bound, that is potentially tighter and defined for a larger class of distributions. Examples of applications in signal processing and information theory illustrate the usefulness of the proposed bounds in practice.

cs.IT↗
arXiv2020

A different approach for choosing a threshold in peaks over threshold

Abstract In Extreme Value methodology the choice of threshold plays an important role in efficient modelling of observations exceeding the threshold. The threshold must be chosen high enough to ensure an unbiased extreme value index but choosing the threshold too high results in uncontrolled variances. This paper investigates a generalized model that can assist in the choice of optimal threshold values in the γpositive domain. A Bayesian approach is considered by deriving a posterior distribution for the unknown generalized parameter. Using the properties of the posterior distribution allows for a method to choose an optimal threshold without visual inspection.

stat.ME↗
arXiv2020

Fourier Analysis and Benford Random Variables

This paper has several major purposes. The central purpose is to describe the "Benford analysis" of a positive random variable and to summarize some results from investigations into base dependence of Benford random variables. The principal tools used to derive these results are Fourier series and Fourier transforms, and a second major purpose of this paper is to present an introductory exposition about these tools. My motivation for writing this paper is twofold. First, I think the theory of Benford random variables and the Benford analysis of a positive random variable are interesting and deserve to be better known. Second, I think that Benford analysis provides a really excellent illustration of the utility of Fourier series and transforms, and reveals certain interconnections between series and transforms that are not obvious from the usual way these subjects are introduced.

stat.OT↗
arXiv2020

Bringing Visual Inference to the Classroom

In the classroom, we traditionally visualize inferential concepts using static graphics or interactive apps. For example, there is a long history of using apps to visualize sampling distributions. Recent developments in statistical graphics have created an opportunity to bring additional visualizations into the classroom to hone student understanding. Specifically, the lineup protocol for visual inference provides a framework for students see the difference between signal and noise by embedding a plot of observed data in a field of null (noise) plots. Lineups have proven valuable in visualizing randomization/permutation tests, diagnosing models, and even conducting valid inference when distributional assumptions break down. This paper provides an overview of how the lineup protocol for visual inference can be used to hone understanding of key statistical topics throughout the statistics curricula.

stat.OT↗
arXiv2020

Continuous limits of Heterogeneous Continuous Time Random Walk model

Continuous time random Walk model has been versatile analytical formalism for studying and modeling diffusion processes in heterogeneous structures, such as disordered or porous media. We are studying the continuous limits of Heterogeneous Continuous Time Random Walk model, when a random walk is making jumps on a graph within different time-length. We apply the concept of a generalized master equation to study heterogeneous continuous-time random walks on networks. Depending on the interpretations of the waiting time distributions the generalized master equation gives different forms of continuous equations.

cond-mat.stat-mech↗
arXiv2020

Predicting First Passage Percolation Shapes Using Neural Networks

Many random growth models have the property that the set of discovered sites, scaled properly, converges to some deterministic set as time grows. Such results are known as shape theorems. Typically, not much is known about the shapes. For first passage percolation on $\mathbb{Z}^d$ we only know that the shape is convex, compact, and inherits all the symmetries of $\mathbb{Z}^d$. Using simulated data we construct and fit a neural network able to adequately predict the shape of the set of discovered sites from the mean, standard deviation, and percentiles of the distribution of the passage times. The purpose of the note is two-fold. The main purpose is to give researchers a new tool for \textit{quickly} getting an impression of the shape from the distribution of the passage times -- instead of having to wait some time for the simulations to run, as is the only available way today. The second purpose of the note is simply to introduce modern machine learning methods into this area of discrete probability, and a hope that it stimulates further research.

stat.ML↗
arXiv2020

Monitoring of process and risk-adjusted medical outcomes using a multi-stage MEWMA chart

Most statistical process control programmes in healthcare focus on surveillance of outcomes at the final stage of a procedure, such as mortality or failure rates. Such an approach ignores the multi-stage nature of these procedures, in which a patient progresses through several stages prior to the final stage. In this paper, we develop a multi-stage control chart based on a multivariate exponentially weighted moving average (EWMA) test statistic derived from score equations. This allows simultaneous monitoring of all intermediate and final stage outcomes of a healthcare process, with adjustment for underlying patient risk factors and dependence between outcome variables. Use of the EWMA test statistics allows quick detection of small gradual changes in any part of the process. Three advantages of the approach are: better understanding of how outcomes at different stages relate to each other, explicit monitoring of upstream stage outcomes may help curtail trends that lead to poorer end-stage outcomes and understanding the impact of each stage can help determine the most effective allocation of quality improvement resources. Simulations are performed to test the control charts under various types of hypothesised shifts, and the results are summarised using out-of-control average run lengths.

stat.ME↗
arXiv2021

Inference in Bayesian Additive Vector Autoregressive Tree Models

Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. We apply our model to two datasets. The first application shows that the BAVART model yields highly competitive forecasts of the US term structure of interest rates. In a second application, we estimate our model using a moderately sized Eurozone dataset to investigate the dynamic effects of uncertainty on the economy.

econ.EM↗
arXiv2020

A Statistical Overview on Data Privacy

The eruption of big data with the increasing collection and processing of vast volumes and variety of data have led to breakthrough discoveries and innovation in science, engineering, medicine, commerce, criminal justice, and national security that would not have been possible in the past. While there are many benefits to the collection and usage of big data, there are also growing concerns among the general public on what personal information is collected and how it is used. In addition to legal policies and regulations, technological tools and statistical strategies also exist to promote and safeguard individual privacy, while releasing and sharing useful population-level information. In this overview, I introduce some of these approaches, as well as the existing challenges and opportunities in statistical data privacy research and applications to better meet the practical needs of privacy protection and information sharing.

cs.CR↗