55 research outputs found
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Binomial-Beta Hierarchical Models for Ecological Inference
he authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King’s ecological inference model. The new approach reveals some features of the data that King’s approach does not, can be easily generalized to more complicated problems such as general R C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the significance of the covariates. It may also be better suited to cases in which the observed aggregate cells are estimated from very few observations or have some forms of measurement error. This article also
provides an example of a hierarchical model in which the statistical idea of “borrowing strength” is used not merely to increase the efficiency of the estimates but to enable the data analyst to obtain estimates.Statistic
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
This paper provides a formal evaluation of the predictive performance of a
model (and its various updates) developed by the Institute for Health Metrics
and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for
each state in the United States. The IHME models have received extensive
attention in social and mass media, and have influenced policy makers at the
highest levels of the United States government. For effective policy making the
accurate assessment of uncertainty, as well as accurate point predictions, are
necessary because the risks inherent in a decision must be taken into account,
especially in the present setting of a novel disease affecting millions of
lives. To assess the accuracy of the IHME models, we examine both forecast
accuracy as well as the predictive performance of the 95% prediction intervals
provided by the IHME models. We find that the initial IHME model underestimates
the uncertainty surrounding the number of daily deaths substantially.
Specifically, the true number of next day deaths fell outside the IHME
prediction intervals as much as 70% of the time, in comparison to the expected
value of 5%. In addition, we note that the performance of the initial model
does not improve with shorter forecast horizons. Regarding the updated models,
our analyses indicate that the later models do not show any improvement in the
accuracy of the point estimate predictions. In fact, there is some evidence
that this accuracy has actually decreased over the initial models. Moreover,
when considering the updated models, while we observe a larger percentage of
states having actual values lying inside the 95% prediction intervals (PI), our
analysis suggests that this observation may be attributed to the widening of
the PIs. The width of these intervals calls into question the usefulness of the
predictions to drive policy making and resource allocation
Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology
This article considers the problem of analyzing associations between power
spectra of multiple time series and cross-sectional outcomes when data are
observed from multiple subjects. The motivating application comes from sleep
medicine, where researchers are able to non-invasively record physiological
time series signals during sleep. The frequency patterns of these signals,
which can be quantified through the power spectrum, contain interpretable
information about biological processes. An important problem in sleep research
is drawing connections between power spectra of time series signals and
clinical characteristics; these connections are key to understanding biological
pathways through which sleep affects, and can be treated to improve, health.
Such analyses are challenging as they must overcome the complicated structure
of a power spectrum from multiple time series as a complex positive-definite
matrix-valued function. This article proposes a new approach to such analyses
based on a tensor-product spline model of Cholesky components of
outcome-dependent power spectra. The approach flexibly models power spectra as
nonparametric functions of frequency and outcome while preserving geometric
constraints. Formulated in a fully Bayesian framework, a Whittle likelihood
based Markov chain Monte Carlo (MCMC) algorithm is developed for automated
model fitting and for conducting inference on associations between outcomes and
spectral measures. The method is used to analyze data from a study of sleep in
older adults and uncovers new insights into how stress and arousal are
connected to the amount of time one spends in bed
Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler
The enormous Nazi voting literature rarely builds on modern statistical or economic research. By adding these approaches, we find that the most widely accepted existing theories of this era cannot distinguish the Weimar elections from almost any others in any country. Via a retrospective voting account, we show that voters most hurt by the depression, and most likely to oppose the government, fall into separate groups with divergent interests. This explains why some turned to the Nazis and others turned away. The consequences of Hitler's election were extraordinary, but the voting behavior that led to it was no
Naked singularities and Seifert's conjecture
It is shown that for a general nonstatic spherically symmetric metric of the
Kerr-Schild class several energy-momentum complexes give the same energy
distribution as in the Penrose prescription, obtained by Tod. This result is
useful for investigating the Seifert conjecture for naked singularities. The
naked singularity forming in the Vaidya null dust collapse supports the Seifert
conjecture. Further, an example and a counterexample to this conjecture are
presented in the Einstein massless scalar theory.Comment: RevTex, no figures, new results included, published in Physical
Review D 60, 104041 (1999
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