scholarly journals Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model

2019 ◽  
Vol 11 (7) ◽  
pp. 841 ◽  
Author(s):  
Fei Yao ◽  
Jiansheng Wu ◽  
Weifeng Li ◽  
Jian Peng
Keyword(s):  
Statistical Model ◽  
Fixed Effects ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Cross Validation ◽  
Infrared Imaging ◽  
Second Stage ◽  
Independent Variable ◽  
Fixed Effects Regression ◽  
Fold Cross Validation

Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.

Modelling of a Stochastic Spatiotemporal Variable in Transport Domain

2021 ◽  
Vol 03 (01) ◽  
pp. 25-31
Author(s):  
Peter Krammer ◽  
Marcel Kvassay ◽  
Ladislav Hluchý
Keyword(s):  
Regression Models ◽  
Cross Validation ◽  
Distribution Characteristics ◽  
Spatiotemporal Modelling ◽  
Bike Sharing ◽  
Day Of The Week ◽  
Irregular Variation ◽  
Fold Cross Validation ◽  
Transport Domain ◽  
Cumulative Sums

In this article, building on our previous work, we engage in spatiotemporal modelling of transport demand in the Montreal metropolitan area over the period of six years. We employ classical machine learning and regression models, which predict bike-sharing demand in the form of daily cumulative sums of bike trips for each considered docking station. Hourly estimates of demand are then determined by considering the statistical distribution of demand across individual hours of an average day. In order to capture seasonal and other regular variation of demand, longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also used as input attributes. We initially conjectured that weather would be an important source of irregular variation in bike-sharing demand, and subsequently included several available meteorological variables in our models. We validated our models by Hold-Out and 10-Fold Cross-Validation, with encouraging results.

scholarly journals Interactions in Fixed Effects Regression Models

2020 ◽  
pp. 004912412091493
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Monte Carlo ◽  
Time Constant ◽  
Fixed Effects ◽  
Regression Models ◽  
Time Varying ◽  
Unit Level ◽  
Monte Carlo Experiments ◽  
Product Term ◽  
Fixed Effects Regression ◽  
Effect Heterogeneity

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, algebraic transformations reveal that this strategy does not yield a within-unit estimator. Instead, the standard FE interaction estimator reflects unit-level differences of the interacted variables. This property allows interactions of a time-constant variable and a time-varying variable in FE to be estimated but may yield unwanted results if both variables vary within units. In such cases, Monte Carlo experiments confirm that the standard FE estimator of x ⋅ z is biased if x is correlated with an unobserved unit-specific moderator of z (or vice versa). A within estimator of an interaction can be obtained by first demeaning each variable and then demeaning their product. This “double-demeaned” estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T > 2.

Global observation-based climatology of precipitation occurrence and peak intensity

2020 ◽  
Author(s):  
Hylke Beck ◽  
Seth Westra ◽  
Eric Wood
Keyword(s):  
Land Surface ◽  
Regression Models ◽  
Cross Validation ◽  
Climate Models ◽  
Daily Precipitation ◽  
State Of The Art ◽  
Coefficient Of Determination ◽  
Peak Intensity ◽  
Uncertainty Estimates ◽  
Fold Cross Validation

<p>We introduce a unique set of global observation-based climatologies of daily precipitation (<em>P</em>) occurrence (related to the lower tail of the <em>P</em> distribution) and peak intensity (related to the upper tail of the <em>P</em> distribution). The climatologies were produced using Random Forest (RF) regression models trained with an unprecedented collection of daily <em>P</em> observations from 93,138 stations worldwide. Five-fold cross-validation was used to evaluate the generalizability of the approach and to quantify uncertainty globally. The RF models were found to provide highly satisfactory performance, yielding cross-validation coefficient of determination (<em>R</em><sup>2</sup>) values from 0.74 for the 15-year return-period daily <em>P</em> intensity to 0.86 for the >0.5 mm d<sup>-1</sup> daily <em>P</em> occurrence. The performance of the RF models was consistently superior to that of state-of-the-art reanalysis (ERA5) and satellite (IMERG) products. The highest <em>P</em> intensities over land were found along the western equatorial coast of Africa, in India, and along coastal areas of Southeast Asia. Using a 0.5 mm d<sup>-1</sup> threshold, <em>P</em> was estimated to occur 23.2 % of days on average over the global land surface (excluding Antarctica). The climatologies including uncertainty estimates will be released as the Precipitation DISTribution (PDIST) dataset via www.gloh2o.org/pdist. We expect the dataset to be useful for numerous purposes, such as the evaluation of climate models, the bias correction of gridded <em>P</em> datasets, and the design of hydraulic structures in poorly gauged regions.</p>

scholarly journals Language Variation Suite: A theoretical and methodological contribution for linguistic data analysis

2016 ◽  
Vol 1 ◽  
pp. 29 ◽  
Author(s):  
Olga Scrivner ◽  
Manuel Díaz-Campos
Keyword(s):  
Fixed Effects ◽  
Regression Models ◽  
Quantitative Methods ◽  
Language Variation ◽  
Mixed Effects ◽  
Logistic Regression Models ◽  
Linguistic Data ◽  
Diachronic Study ◽  
Phonetic Context ◽  
Fixed Effects Regression

In recent years there has been growing interest in quantitative methods for analyzing linguistic data.  Advanced multifactorial statistical analyses, such as inferential trees and mixed-effects logistic regression models, have become more accessible for linguistic research as a result of the availability of an open source programming environment provided by the statistical software R. In the present paper, we introduce a novel toolkit, Language Variation Suite, a software program that offers a friendly environment for conducting quantitative analyses. We demonstrate how theory built on traditional monofactorial analysis can be extended to macro and micro multifactorial approaches allowing for a deeper understanding of language variation. The focus of the analysis is based on intervocalic /d/ deletion in Spanish from the Diachronic Study of the Speech of Caracas 1987 and 2004-2010. In contrast to traditional methodological approaches we have treated intervocalic /d/ as a continuous dependent variable according to the intensity ratio measurements obtained. Furthermore, we have integrated various syntactic, phonetic and sociolinguistic factors. Non-parametric and fixed-effects regression models revealed that overall age (younger speakers), sex (male speakers), phonetic context (low vowels), token frequency and morphosyntactic category (past participles) have a significant effect on the lenition of intervocalic /d/. In contrast, the mixed-effects model selected only phonetic context, frequency and category, showing that individual speaker variation is higher than group variation.

scholarly journals On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Kosuke Imai ◽  
In Song Kim
Keyword(s):  
Panel Data ◽  
Causal Inference ◽  
Fixed Effects ◽  
Regression Models ◽  
Applied Research ◽  
Causal Effects ◽  
Difference In Differences ◽  
The Difference ◽  
Fixed Effects Regression ◽  
Time Specific

Abstract The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.

scholarly journals Gendered Wealth Losses after Dissolution of Cohabitation but not Marriage in Germany

2019 ◽  
Author(s):  
Diederik Boertien ◽  
Philipp M. Lersch
Keyword(s):  
Gender Inequality ◽  
Fixed Effects ◽  
Regression Models ◽  
Legal Protection ◽  
Economic Consequences ◽  
Union Dissolution ◽  
Economic Wealth ◽  
Marital Property ◽  
Fixed Effects Regression ◽  
Property Regimes

Objective: To document gender differences in how economic wealth changes following the dissolution of marriage and cohabitation in Germany.Background: Wealth can be an important resource to deal with the adverse economic consequences of union dissolution. Marital property regimes usually ensure that both partners receive a share of the couples’ wealth following a divorce. The dissolution of cohabiting unions is not governed by such rules in most countries, including Germany, which may lead to a more unequal division of wealth following the dissolution of cohabitation as compared to marriage.Method: The analysis consists of multivariable fixed-effects regression models based on longitudinal data from the German Socio-Economic Panel (N = 6,388 individuals) for the years 2002 to 2017.Results: Changes in wealth are relatively similar for men and women after the dissolution of marriage. The dissolution of cohabiting unions is related to losses in wealth for women, but not for men. Controlling for post-dissolution processes, gender inequality increases after the dissolution of cohabitations.Conclusion: Union dissolution is associated with wealth losses. The legal protection of the economically weaker spouse in marriage prevents gender inequality in these wealth losses. Lacking such legal protection, cohabitation is associated with gender inequality in the consequences of dissolution.

Crimes Against Women in India: A District-Level Analysis (1991–2011)

2020 ◽  
pp. 088626052096714
Author(s):  
Aparna P. Lolayekar ◽  
Shaila Desouza ◽  
Pranab Mukhopadhyay
Keyword(s):  
Fixed Effects ◽  
Regression Models ◽  
Nonlinear Effects ◽  
Higher Education Attainment ◽  
Crimes Against Women ◽  
Panel Fixed Effects ◽  
Fixed Effects Regression ◽  
Female Literacy ◽  
Women In India ◽  
Level Analysis

Crimes against women (CAW) in India have been rising despite faster economic growth, higher education attainment, and increasing numbers of women in the economic sphere. This article explores the reasons for the incidence of reported CAW in India. We study five CAW (rape, kidnapping, cruelty, dowry deaths, and molestation), across 35 states and union territories, 594 districts, over three decades (1991–2011). We use panel fixed-effects regression models to explain crime. Our results confirm the importance of female literacy rates, female paid workforce participation, and female–male ratio in understanding crime. We find that these commonly-used socioeconomic variables have nonlinear effects on CAW. Our findings improve upon earlier results that have not explored either spatial distribution or nonlinearity in India. These findings could have significant implications for the policies aiming to reduce CAW.

scholarly journals Improving the Interpretation of Fixed Effects Regression Results

2018 ◽  
Vol 6 (4) ◽  
pp. 829-835 ◽  
Author(s):  
Jonathan Mummolo ◽  
Erik Peterson
Keyword(s):  
Panel Data ◽  
Selection Bias ◽  
Fixed Effects ◽  
Fixed Effects Models ◽  
Independent Variable ◽  
Fixed Effects Regression ◽  
Time Invariant ◽  
Substantive Importance

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

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