Pages that link to "Item:Q3632585"
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The following pages link to High-Resolution Space–Time Ozone Modeling for Assessing Trends (Q3632585):
Displaying 26 items.
- A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling (Q312916) (← links)
- A new method to build spatio-temporal covariance functions: analysis of ozone data (Q345366) (← links)
- Modelling interoccurrence times between ozone peaks in Mexico City in the presence of multiple change points (Q468028) (← links)
- A bivariate space-time downscaler under space and time misalignment (Q542961) (← links)
- A comparison of Bayesian models for daily ozone concentration levels (Q713932) (← links)
- A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration (Q765981) (← links)
- Extracting a common signal in tree ring widths with a semi-parametric Bayesian hierarchical model (Q1618207) (← links)
- A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models (Q1621224) (← links)
- A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC (Q1703834) (← links)
- Regularity and approximation of Gaussian random fields evolving temporally over compact two-point homogeneous spaces (Q2074676) (← links)
- Spatial modeling of day-within-year temperature time series: an examination of daily maximum temperatures in Aragón, Spain (Q2102967) (← links)
- Functional single-index quantile regression models (Q2195823) (← links)
- Hierarchical Bayesian spatio-temporal modeling for \(\mathrm{PM}_{10}\) prediction (Q2241277) (← links)
- Quantifying local creation and regional transport using a hierarchical space-time model of ozone as a function of observed \(\text{NO}_{\text{x}}\), a latent space-time VOC process, emissions, and meteorology (Q2260201) (← links)
- Modelling spatio-temporal variation in sparse rainfall data using a hierarchical Bayesian regression model (Q2419852) (← links)
- Multivariate functional data modeling with time-varying clustering (Q2666063) (← links)
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion) (Q2920273) (← links)
- Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data (Q3391187) (← links)
- Space‐Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality (Q4649060) (← links)
- Considering groups in the statistical modeling of spatio-temporal data (Q5148507) (← links)
- Hierarchical spatially varying coefficient and temporal dynamic process models using<tt>spTDyn</tt> (Q5222376) (← links)
- Modelling spatio-temporal air pollution data from a mobile monitoring station (Q5222498) (← links)
- Assessing exceedance of ozone standards: a space‐time downscaler for fourth highest ozone concentrations (Q6090035) (← links)
- Nonseparable covariance models on circles cross time: a study of Mexico City ozone (Q6626087) (← links)
- Bayesian spatiotemporal modeling for estimating short-term exposure to air pollution in Santiago de Chile (Q6626094) (← links)
- Bayesian Nonparametric Joint Mixture Model for Clustering Spatially Correlated Time Series (Q6636549) (← links)
