A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

2020 ◽  
Author(s):  
Chen-Wei Liu ◽  
Robert Philip Chalmers
Keyword(s):  
Information Matrix ◽  
Observed Information ◽  
Observed Information Matrix ◽  
Diagnostic Models ◽  
Cognitive Diagnostic Models ◽  
Matrix Identity

Recursions for the MMPP Score Vector and Observed Information Matrix

2010 ◽  
Vol 26 (4) ◽  
pp. 649-665 ◽  
Author(s):  
Christopher J. Willy ◽  
William J. J. Roberts ◽  
Thomas A. Mazzuchi ◽  
Shahram Sarkani
Keyword(s):  
Information Matrix ◽  
Score Vector ◽  
Observed Information ◽  
Observed Information Matrix

Information matrix estimation procedures for cognitive diagnostic models

2018 ◽  
Vol 72 (1) ◽  
pp. 18-37 ◽  
Author(s):  
Yanlou Liu ◽  
Tao Xin ◽  
Björn Andersson ◽  
Wei Tian
Keyword(s):  
Information Matrix ◽  
Matrix Estimation ◽  
Diagnostic Models ◽  
Estimation Procedures ◽  
Cognitive Diagnostic Models

scholarly journals The Stochastic Stationary Root Model

Econometrics ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 39
Author(s):  
Andreas Hetland
Keyword(s):  
Likelihood Function ◽  
Asymptotic Properties ◽  
Information Matrix ◽  
Autoregressive Process ◽  
Var Model ◽  
Cointegrated Var ◽  
Observed Information ◽  
Observed Information Matrix ◽  
Root Model ◽  
Ml Estimator

We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.

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