Semiparametric Bayes analysis of longitudinal data treatment models

By Siddhartha Chib, Barton H. Hamilton. 2002

120 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4161009abf380a08af6d2474e238832421746a7b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “proximate arbitrary distributions. For exible longitudinal data analysis, Silva (2016) uses Gaussian process to model longitudinal outcome under dierent levels of interventions. In another example, Chib and Hamilton (2002) uses the Dirichlet Process prior to add exiblility in representing the outcom”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “In another example, Chib and Hamilton (2002) uses the Dirichlet Process prior to add flexiblility in representing the outcome and the treatment effects.”
Semiparametric Bayesian Inference of Long‐Memory Stochastic Volatility Models (2004, methodology): “Instead, we adopt the semiparametric Bayesian approach of Tiwari et al. (1988), Chib and Hamilton (2002), and Hirano (2002), where the unknown distribution of the innovations are modelled non-parametrically by a Dirichlet process.”
Conditional (2019, unknown): “In summary, the main contributions of our work are described as follows: • We propose a Latent Diffusion-based ID Sampler based on DDIM, capable of sampling multidimensional identity embeddings with diversity and realism, which serve as anonymized identity embeddings for subsequent replacement.”
Bayesian dynamic density estimation (2008, background): “…processes include finance, econometrics, epidemiology, genetics and survival analysis among others (Müller et al. 2004; Kacperczyk et al. 2004; Chib and Hamilton 2002; Hirano 2002; Dunson 2005; Medvedovic and Sivaganesan 2002; Dunson et al. 2007a; Kottas et al. 2002) as well as (Bigelow and…”
Stick-breaking autoregressive processes (2011, background): “Early applications in economics include autoregressive panel data models (Hirano, 2002), longitudinal data treatment models (Chib and Hamilton, 2002) and stochastic frontiers (Griffin and Steel, 2004). and James, 2001, 2003) (which will be discussed in section 2), and Normalized Random Measures…”
Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, methodology): “(2011)), is now widely used due to the availability of efficient algorithms for posterior computations (Escobar and West, 1995; MacEachern and Müller, 1998; Papaspiliopoulos and Roberts, 2008; Taddy, 2010), including but not limited to applications in time series settings (Hirano, 2002; Chib and Ham”
Order-Based Dependent Dirichlet Processes (2006, methodology): “Some recent examples include applications in econometrics (Chib and Hamilton 2002; Hirano 2002), medicine (Kottas, Branco, and Gelfand 2002), health (O’Hagan and Stevens 2003), auditing (Laws and O’Hagan 2002), animal breeding (van der Merwe and Pretorius 2003), survival analysis (Doss and Huffer…”
Analysis of treatment response data without the joint distribution of potential outcomes (2007, methodology): “Subsequently, within the context of the joint modeling of the potential outcomes, Chib and Hamilton (2000, 2002) provided a Bayesian analysis of generalized versions of the basic model relevant for panel data, binary outcomes and ordinal treatments, under weak distributional assumptions, while…”
A Bayesian mixed logit-probit model for multinomial choice (2008, methodology): “Some recent applications of univariate DPMs include epidemiology (Dunson 2005), genetics (Medvedovic and Sivaganesan 2002), medicine (Kottas, Branco, and Gelfand 2002), finance (Kacperczyk, Damien, and Walker 2003), and stochastic volatility modeling (Jensen and Maheu 2007). In the microeconometric ”
Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys (2013, methodology): “We note that Dirichlet process mixture models (Antoniak, 1974) are used in many fields including, for example, econometrics (Chib & Hamilton, 2002; Hirano, 2002), social science (Kyung, Gill, & Casella, 2010), and finance (Rodrı́guez & Dunson, 2011).”
Bayesian semiparametric stochastic volatility modeling (2010, methodology): “1 Examples of the DPM being used in economics include Chib and Hamilton (2002), Conley et al. (2008), Griffin and Steel (2004), Hirano (2002), Jensen (2004), Kacperczyk et al.”