Isotone additive latent variable models

Sardy, Sylvain; Victoria‐Feser, Maria‐Pia

doi:10.1007/s11222-011-9262-z

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…These predictions for the z i are found in a premilinary step where we perform a Factor Analysis on the data as if they were normal and compute the Bartlett predictions of the latent scores (Bartlett, 1950). A similar idea has been used in Sardy and Victoria-Feser (2012). This initial estimator is thus obviously not consistent but is easy to compute.…”

Section: Application To Generalized Linear Latent Variable Modelsmentioning

confidence: 99%

Simulation-Based Bias Correction Methods for Complex Models

Guerrier

Dupuis-Lozeron

Victoria‐Feser

2018

Journal of the American Statistical Association

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Along the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators.In this context, we extend and provide refinements on the known bias 1 correction properties of two simulation based methods, respectively indirect inference and bootstrap, each with two alternatives. These results allow one to build a framework defining simulation based estimators that can be implemented for complex models. Indeed, based on a biased or even inconsistent estimator, several simulation based methods can be used to define new estimators that are both consistent and with reduced finite sample bias. This framework includes the classical method of indirect inference for bias correction without requiring specification of an auxiliary model. We demonstrate the equivalence between one version of the indirect inference and the iterative bootstrap, both correct sample biases up to the order n −3 .The iterative method can be thought of as a computationally efficient algorithm to solve the optimization problem of the indirect inference.Our results provide different tools to correct the asymptotic as well as finite sample biases of estimators and give insight on which method should be applied for the problem at hand. The usefulness of the proposed approach is illustrated with the estimation of robust income distributions and generalized linear latent variable models.

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Section: Application To Generalized Linear Latent Variable Modelsmentioning

confidence: 99%

Simulation-Based Bias Correction Methods for Complex Models

Guerrier

Dupuis-Lozeron

Victoria‐Feser

2018

Journal of the American Statistical Association

Self Cite

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“…However, these models are parametric and their structure needs to be specified by the user. A first step towards a fully nonparametric approach is done by Sardy and Victoria-Feser (2012).…”

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Nonparametric additive factor models

Bodelet,

Shan

2020

Preprint

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This paper proposes a nonparametric additive factor model where the common components depend on the latent factors through unknown smooth functions. Our approach is novel in the literature on nonlinear factor models as we propose a general and rigorous framework for identification, specify a general nonparametric and flexible estimation procedure based on sieve methods, and derive consistency results. The key point of our strategy relies on the specification of an asymptotic parameter space for the factors. Estimation is then obtained by using sieve approximations of this infinite dimensional factor space. We prove convergence of the sieve estimators as both time and cross-sectional sizes increase at appropriate rates. The finite sample performance of the estimators is illustrated in extensive numerical experiments. Finally, we show relevance and usefulness of our method by an application to a nonlinear CAPM on S&P500 data.

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MTP2 and Partial Correlations in Monotone Higher-Order Factor Models

Ellis

2015

Quantitative Psychology Research

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Isotone additive latent variable models

Cited by 3 publications

References 58 publications

Simulation-Based Bias Correction Methods for Complex Models

Simulation-Based Bias Correction Methods for Complex Models

Nonparametric additive factor models

MTP2 and Partial Correlations in Monotone Higher-Order Factor Models

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