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Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Media regarding COVID-19 fatality counts is crucial, affecting policy and health measures nationwide. However, misinformation regarding other causes of death has led to dubious claims about the seriousness of the coronavirus. This research aims to identify the changes in a dozen causes of death during the pandemic using CDC data from 1999 to 2020. Using the Exponential Triple Smoothing (ETS) algorithm, this project estimated the mortality of eleven causes of death for 2020 under the assumption of no COVID-19 pandemic. Using Power BI and Tableau, this data was visualized together with 2020 actual death counts to determine which causes of death were significantly impacted by the coronavirus. The dashboard revealed an increase in several causes of death including Alzheimer's Disease and Diabetes, a decrease in Chronic Lower Respiratory Disease deaths, and a slight increase in Influenza deaths. These findings, while at odds with much of the media surrounding COVID-19 mortality, are corroborated by adjacent scientific research. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities (i.e., genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online. 1 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc. [ABSTRACT FROM AUTHOR]

The article discusses a study which proposed a method for smoothing parameter selection for nonparametric regression with spatially correlated errors. A pilot fit to the data and the estimation of a parametric covariance model are used. The study illustrated the methodology using water chemistry data collected in a sample of lakes in the Northeastern U.S.

The method presented here assumes that the variable under consideration is a weighted moving average of annual values, with weights to be estimated by means of quadratic programming. This multivariate (MV) method is used to estimate quarterly income figures on the basis of annual income data in quarterly consumption functions for the U.S. and the Netherlands. The MV method appears to perform slightly better than the competing single-variate (SV) smoothing methods of Feibes, Boot and Lisman with respect to parameter estimates, and smallness and randomness of the differences between calculated and observed values of consumption. [ABSTRACT FROM AUTHOR]