Sparse spatial autoregressions

By R. Pace, Ronald P. Barry. 1997

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Citations in the corpus

Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020, methodology): “California Housing dataset [27] is a canonical machine learning dataset where the task is to predict the median price of houses (in million dollars) in each California district.”
Local Linear Forests (2018, methodology): “The data contain summary statistics for housing prices in a California district from 1990, first introduced in a paper from Pace and Barry [1997] and downloaded from Kaggle.”
A method for spatial–temporal forecasting with an application to real estate prices (2000, background): “Pace and Barry (1997a) found estimates of thehousing transaction data.”
Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables (2007, methodology): “To illustrate the proposed methodology, we make use of the California Housing Data, introduced by Pace and Barry (1997) as a real-world demonstration. These data consist of the median house prices along with demographic variables in 20,640 regions of California, based on U.S. Census data. We removed”
Spatial Dependence, Housing Submarkets, and House Price Prediction (2007, methodology): “These include geostatistical models such as those applied in real est te by Dubin (1998) or Basu and Thibodeau (1998) and the lattice models that have been refined and applied by Pace and his colleagues (e.g., Pace and Barry, 1997a).”
On Embeddings for Numerical Features in Tabular Deep Learning (2022, unknown): “In this work, we have demonstrated that embeddings for numerical features are an important design aspect of tabular DL architectures.”
Tilted Empirical Risk Minimization (2020, methodology): “We investigate the performance of TERM on two datasets (cal-housing [48] and abalone [15]) used in Yu et al.”
TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2022, unknown): “, 2022) contains eight attributes of 20,640 districts in California and the goal is to predict the median house value in each district (Pace and Barry, 1997).”
Differentially Private M-Estimators (2011, background): “The inference problem of interest is to study the relationship between housing price and other variables [14].”
Revisiting Deep Learning Models for Tabular Data (2021, background): “The datasets include: California Housing (CA, real estate data, Kelley Pace and Barry (1997)), Adult (AD, income estimation, Kohavi (1996)), Helena (HE, anonymized dataset, 2013)).”
Panel data models with spatially correlated error components (2007, methodology): “All rights reserved. doi:10.1016/j.jeconom.2006.09.004 Cochrane–Orcutt transformation, as well as transformations utilized in the estimation of classical error component models.”
Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM) (2006, methodology): “Many approaches have been proposed to speed up this step using eigendecomposition (Ord, 1975, improved in Smirnov and Anselin, 2001), LU or Cholesky decomposition (Pace, 1997, Pace and Barry, 1997a, 1997b), or approximation functions (e.g., Barry and Pace, 1999).”
On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors (2013, unknown): “…(1999) (2001) (2004) (2007) (2010), Kelejian et al. (2004), Kapoor et al. (2007), Lee (2002) (2003) (2004), LeSage (1997, 2000), Lin and Lee (2010), Lin and Lee (2010), Lin and Lee (2011), Pace and Barry (1997), Pinkse and Slade (1998), Pinkse et al. (2002), Rey and Boarnet (2004), and Yang (2010).”
Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Micro-Level Data (2000, background): “Pace and Barry (1997) have obtained ML estimates for problems in the order of 20,000 observations by repeatedly evaluating the determinant in equation (2) directly rather than attempting to calculate eigenvalues.”
Deep Neural Networks and Tabular Data: A Survey (2021, methodology): “Finally, we utilize the California Housing dataset [194], which contains information about a number of properties.”