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The end of average : how we succeed in a world that values sameness
\"Weaving science, history, and his experiences as a high school dropout, Rose brings to life the untold story of how we came to embrace the scientifically flawed idea that averages can be used to understand individuals and offers a powerful alternative: the three principles of individuality\"-- Provided by publisher.
Book
Metalearners for estimating heterogeneous treatment effects using machine learning
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.
Journal Article
Characteristics of the Molecular Weight of Polyhexamethylene Guanidine (PHMG) Used as a Household Humidifier Disinfectant
(1) Background: Household humidifier disinfectant (HD) brands containing polyhexamethylene guanidine (PHMG) have been found to cause the most HD-associated lung injuries (HDLIs) in the Republic of Korea. Nevertheless, no study has attempted to characterize the potential association of the health effects, including HDLI, with the physicochemical properties of PHMG dissolved in different HD brands. This study aimed to characterize the molecular weight (MW) distribution, the number-average molecular weight (Mn), the weight-average molecular weight (Mw), and the structural types of PHMG used in HD products. (2) Methods: Quantitative measurements were made using matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS). The Mn, Mw, and MW distributions were compared among various HD products. (3) Results: The mean Mn and Mw were 542.4 g/mol (range: 403.0–692.2 g/mol) and 560.7 g/mol (range: 424.0–714.70 g/mol), respectively. The degree of PHMG oligomerization ranged from 3 to 7. The MW distribution of PHMG indicated oligomeric compounds regardless of the HD brands. (4) Conclusions: Based on the molecular weight distribution, the average molecular weight of PHMG, and the degree of polymerization, the PHMG collected from HDLI victims could be regarded as an oligomer. PHMG, as used in household humidifiers, should not be exempted from toxic chemical registration as a polymer. Further study is necessary to examine the association of PHMG oligomeric compounds and respiratory health effects, including HDLI.
Journal Article
Non‐parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors
Periodicity and trend are features describing an observed sequence, and extracting these features is an important issue in many scientific fields. However, it is not an easy task for existing methods to analyse simultaneously the trend and dynamics of the periodicity such as time varying frequency and amplitude, and the adaptivity of the analysis to such dynamics and robustness to heteroscedastic dependent errors are not guaranteed. These tasks become even more challenging when there are multiple periodic components. We propose a non‐parametric model to describe the dynamics of multicomponent periodicity and investigate the recently developed synchro‐squeezing transform in extracting these features in the presence of a trend and heteroscedastic dependent errors. The identifiability problem of the non‐parametric periodicity model is studied, and the adaptivity and robustness properties of the synchro‐squeezing transform are theoretically justified in both discrete and continuous time settings. Consequently we have a new technique for decoupling the trend, periodicity and heteroscedastic, dependent error process in a general non‐parametric set‐up. Results of a series of simulations are provided, and the incidence time series of varicella and herpes zoster in Taiwan and respiratory signals observed from a sleep study are analysed.
Journal Article
Highly public anti-Black violence is associated with poor mental health days for Black Americans
Highly public anti-Black violence in the United States may cause widely experienced distress for Black Americans. This study identifies 49 publicized incidents of racial violence and quantifies national interest based on Google searches; incidents include police killings of Black individuals, decisions not to indict or convict the officer involved, and hate crime murders. Weekly time series of population mental health are produced for 2012 through 2017 using two sources: 1) Google Trends as national search volume for psychological distress terms and 2) the Behavioral Risk Factor Surveillance System (BRFSS) as average poor mental health days in the past 30 d among Black respondents (mean weekly sample size of 696). Autoregressive moving average (ARMA) models accounted for autocorrelation, monthly unemployment, season and year effects, 52-wk lags, news-related searches for suicide (for Google Trends), and depression prevalence and percent female (for BRFSS). National search interest varied more than 100-fold between racial violence incidents. Black BRFSS respondents reported 0.26more poormental health days during weekswith two or more racial incidents relative to none, and 0.13 more days with each log10 increase in national interest. Estimates were robust to sensitivity tests, including controlling for monthly number of Black homicide victims and weekly search interest in riots. As expected, racial incidents did not predict average poor mental health days among White BRFSS respondents. Results with national psychological distress from Google Trends were mixed but generally unsupportive of hypotheses. Reducing anti-Black violence may benefit Black Americans’ mental health nationally.
Journal Article
Stock price prediction: comparison of different moving average techniques using deep learning model
The stock market is changing quickly, and its nonlinear characteristics make stock price prediction difficult. Predicting stock prices is challenging due to several factors, including a company’s financial performance, unforeseen circumstances, general economic conditions, politics, current assets, global situation, etc. Despite these terms, sufficient data are available to identify stock price movement trends using the different technical approaches. In this research, we empirically analyzed long short-term memory (LSTM) networks in the context of time-series prediction. Our investigation leveraged a diverse set of real-world datasets and provided quantitative insights into the performance of LSTMs. Across a spectrum of time-series forecasting tasks, LSTM models demonstrated an impressive mean absolute error (MAE) reduction of 23.4% compared to traditional forecasting methods. Specifically, LSTM achieved an average prediction accuracy of 89.7% in financial market predictions, outperforming baseline models by a significant margin. The aim is to obtain a value that can be compared to the present price of an asset to determine whether it is overvalued or undervalued, which anticipates the price patterns by analyzing previous market information, such as price and volume, compared to this stock analysis approach.
Journal Article
The Sequence G-asymptotic Average Shadowing Property with G-chain transitive
Let ( M , d ) be a compact metric @-space, Φ : M → M be a continuous map. This paper aims to study the idea of the sequence-asymptotic average shadowing property ( s i -AASP ) for a continuous map on-space, ( s i :i ≥1 be a given positive integers sequence, where s 0 = 0 ) and achieves the relative of the s i -AASP with the sequence AASP ( s i -AASP ). We prove that if ( M, d ) are metric 1 -spaces, (X, d) then metric 2 -spaces and ƒ : 1 χ → Μ , ψ : 2 x Χ → Χ are continuous maps, then ƒ has the 1 s i -AASP and ψ has the 2 s i -AASP if and only if the product ƒ x ψ has the 1 x 2 s i -AASP. Also, we show that if Φ has the s i -AASP then Φ is-chain transitive.
Journal Article
Wind power prediction based on variational mode decomposition multi-frequency combinations
Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition (VMD). We use a back propagation neural network (BPNN), autoregressive moving average (ARMA) model, and least squares support vector machine (LS-SVM) to predict high, intermediate, and low frequency components, respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value. Finally, the prediction performance of the single prediction models (ARMA, BPNN, LS-SVM) and the decomposition prediction models (EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error, and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.
Journal Article
Marginal and average considerations in LCA and their role for defining emission factors and characterization factors
Introduction
In the literature on LCA, one often finds the terms “marginal” and “average,” often in combination with words like “data,” “process,” “emission,” or “characterisation factor.” However, the meaning of these terms appears to differ between sources. This paper aims to clarify the situation.
Critical analysis
We review the various definitions and interpretations of the terms “marginal” and “average” in economics, LCI and LCIA. We also study the role of various related terms, such as “linear” and “incremental.” It turns out that the term “marginal” is used for characterizing processes in some sources and for characterizing the data that describes processes in other sources. These two interpretations are shown to differ substantially in a hypothetical example. We also note that the situation in the LCIA literature differs markedly from that in the LCI literature.
Conclusion and discussion
We propose to distinguish three concepts, marginal, average, and average marginal, and offer verbal definitions, mathematical equations, and a numerical example with a graphical interpretation. We also draw an agenda to research the implications for the attributional-consequential debate, the development of databases and software, and several other topics. This may also help to bring more insights in the continuing controversy on consequential versus attributional LCA.
Journal Article
Doubly robust estimation of the local average treatment effect curve
We consider estimation of the causal effect of a binary treatment on an outcome, conditionally on covariates, from observational studies or natural experiments in which there is a binary instrument for treatment. We describe a doubly robust, locally efficient estimator of the parameters indexing a model for the local average treatment effect conditionally on covariates V when randomization of the instrument is only true conditionally on a high dimensional vector of covariates X, possibly bigger than V. We discuss the surprising result that inference is identical to inference for the parameters of a model for an additive treatment effect on the treated conditionally on V that assumes no treatment–instrument interaction. We illustrate our methods with the estimation of the local average effect of participating in 401(k) retirement programmes on savings by using data from the US Census Bureau's 1991 Survey of Income and Program Participation.
Journal Article