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To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem—Poisson trend filtering—using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim. Author summary: Instantaneous reproduction number estimation presents many challenges due to data collection, modelling assumptions, and computational burden. Our motivation is to develop a model that produces accurate estimates, is robust to model misspecification, is straightforward to use, and is computationally efficient, even for large counts and long time periods. We propose a convex optimization model with an ℓ1 trend filtering penalty. It couples accurate estimation of the instantaneous reproduction number with desired smoothness. We solve the optimization using the proximal Newton method, which converges rapidly and is numerically stable. Our software, conveniently available in the R package rtestim, can produce estimates in seconds for incidence sequences with hundreds of observations. These estimates are produced for a sequence of tuning parameters and can be selected using a built-in cross validation procedure. [ABSTRACT FROM AUTHOR]

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change. This manuscript evaluates forecasts of laboratory-confirmed influenza hospital admissions, a new target for influenza forecasting in the United States. Across two influenza seasons, the FluSight ensemble is robust compared to submitted models. [ABSTRACT FROM AUTHOR]

Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this article we consider the problem of multi-period forecasting that aims to predict several horizons at once. We propose a novel approach that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression. This methodology was developed for real-time distributed COVID-19 forecasting. We illustrate the proposed technique with the COVIDcast dataset as well as a small simulation example. for this article are available online. [ABSTRACT FROM AUTHOR]

Motivation Methods for the global measurement of transcript abundance such as microarrays and RNA-Seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable insights from such data therefore requires high-dimensional prediction. Existing sparse linear approaches to this challenge have been stunningly successful, but some important issues remain. These methods can fail to select the correct features, predict poorly relative to non-sparse alternatives or ignore any unknown grouping structures for the features. Results We propose a method called SuffPCR that yields improved predictions in high-dimensional tasks including regression and classification, especially in the typical context of omics with correlated features. SuffPCR first estimates sparse principal components and then estimates a linear model on the recovered subspace. Because the estimated subspace is sparse in the features, the resulting predictions will depend on only a small subset of genes. SuffPCR works well on a variety of simulated and experimental transcriptomic data, performing nearly optimally when the model assumptions are satisfied. We also demonstrate near-optimal theoretical guarantees. Availability and implementation Code and raw data are freely available at https://github.com/dajmcdon/suffpcr. Package documentation may be viewed at https://dajmcdon.github.io/suffpcr. Contact daniel@stat.ubc.ca Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

Modern applications require methods that are computationally feasible on large datasets while retaining good statistical properties. Recent work has focused on developing fast and randomized approximations for solving least squares problems when the data are too large to fit into memory easily or when computations are at a premium. Many of these techniques rely on data-driven subsampling or random compression. In this article, we provide new approximate algorithms for solving penalized least squares problems which have improved statistical performance relative to existing methods. We provide the first efficient methods for tuning parameter selection, compare our methods with current approaches via simulation and application, and provide theoretical intuition which makes explicit the impact of approximation on statistical efficiency and demonstrates the necessity of careful parameter tuning. for this article are available online. [ABSTRACT FROM AUTHOR]

High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amount of regularization, often determined by tuning parameters, is integral to achieving good performance. One can choose the tuning parameter in a variety of ways, such as through resampling methods or generalized information criteria. However, the theory supporting many regularized procedures relies on an estimate for the variance parameter, which is complicated in high dimensions. We develop a suite of information criteria for choosing the tuning parameter in lasso regression by leveraging the literature on high-dimensional variance estimation. We derive intuition showing that existing information-theoretic approaches work poorly in this setting. We compare our risk estimators to existing methods with an extensive simulation and derive some theoretical justification. We find that our new estimators perform well across a wide range of simulation conditions and evaluation criteria. [ABSTRACT FROM AUTHOR]

Motivation: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable. Results: We develop a new technique for using the marginal relationship between gene expression measurements and patient survival outcomes to identify a small subset of genes which appear highly relevant for predicting survival, produce a low-dimensional embedding based on this small subset, and amplify this embedding with information from the remaining genes. We motivate our methodology by using gene expression measurements to predict survival time for patients with diffuse large B-cell lymphoma, illustrate the behavior of our methodology on carefully constructed synthetic examples, and test it on a number of other gene expression datasets. Our technique is computationally tractable, generally outperforms other methods, is extensible to other phenotypes, and also identifies different genes (relative to existing methods) for possible future study. [ABSTRACT FROM AUTHOR]

We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These non-asymptotic bounds need only weak assumptions on the data-generating process, yet allow forecasters to select among competing models and to guarantee, with high probability, that their chosen model will perform well. We motivate our techniques with and apply them to standard economic and financial forecasting tools--a GARCH model for predicting equity volatility and a dynamic stochastic general equilibrium model (DSGE), the standard tool in macroeconomic forecasting. We demonstrate in particular how our techniques can aid forecasters and policy makers in choosing models which behave well under uncertainty and mis-specification. [ABSTRACT FROM AUTHOR]

This article is a primer on mortgage finance. It discusses the basics of the mortgage market and mortgage finance. In so doing, it provides useful information that can aid individuals in making better mortgage finance decisions. The discussion and the tools are presented within the context of mortgage finance; however, these same principles and tools can be applied to a wide range of financial decisions. [ABSTRACT FROM AUTHOR]