Analysis of Deviance for Hypothesis Testing in Generalized Partially Linear Models
Bibliographic Data
| ID | 19418010 |
|---|---|
| Authors | Wolfgang Karl Härdle (0000-0001-5600-3014, Center for Applied Statistics and Economics, Humboldt University 10099, Berlin, Germany ()), Li‐Shan Huang (0000-0001-8297-2804, National Tsing Hua University) |
| Year | 2019 |
| Volume | 37 |
| Issue | 2 |
| Pages | 322-333 |
| Publication date | 2019-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2017.1330693 |
| OpenAlex | W2615480457 |
| Language | EN |
| References cited | 25 |
In this study, we develop nonparametric analysis of deviance tools for generalized partially linear models based on local polynomial fitting. Assuming a canonical link, we propose expressions for both local and global analysis of deviance, which admit an additivity property that reduces to analysis of variance decompositions in the Gaussian case. Chi-square tests based on integrated likelihood functions are proposed to formally test whether the nonparametric term is significant. Simulation results are shown to illustrate the proposed chi-square tests and to compare them with an existing procedure based on penalized splines. The methodology is applied to German Bundesbank Federal Reserve data
Additive function · Econometrics · Gaussian · Generalized additive model · Generalized linear model · Linear model · Nonparametric statistics · Statistics · Advanced Statistical Methods and Models · Bayesian Modeling and Causal Inference · Mathematics · Statistical Methods and Inference · Applied Mathematics
| Highly cited | No |
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