Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework

Jerak, Alexander; Wagner, Ştefan

doi:10.1007/s00181-005-0047-0

Cited by 20 publications

(7 citation statements)

References 30 publications

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“…This result is in contradiction with earlier empirical evidence (Graham et al, 2002;Jerak and Wagner, 2003;Reitzig, 2004a), though the ACCSRC variable had only been tested as a determinant of oppositions. However, the same authors as well as Harhoff and Hall (2002) found positive and significant coefficients for accelerated examination requests.…”

Section: Granted Patents Filed 1990-1995 -Robust Standard Errors -Coecontrasting

confidence: 99%

Filing strategies and patent value

Zeebroeck

Potterie

2011

Economics of Innovation and New Technology

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This paper aims at contributing to the literature on the determinants of patent value. First, it puts forward a new potential class of value determinants in the form of filing strategies (encompassing filing routes and drafting styles). Second, it provides empirical evidence suggesting that these strategies are consistently and positively associated with patent value indicators. The empirical implementation relies on a unique dataset (about 250,000 patents granted by the EPO), which allows running large-scale sensitivity tests. The results reveal that the new determinants this paper puts forward -filing strategies -are the most stable of all. They also underline strong dependencies of several 'classical' results to the dependent variables and sampling methodologies, partly explaining several inconsistencies observed in the literature. March 2008 AbstractThis paper aims at contributing to the literature on the determinants of patent value. First, it puts forward a new potential class of value determinants in the form of filing strategies (encompassing filing routes and drafting styles). Second, it provides empirical evidence suggesting that these strategies are consistently and positively associated with patent value indicators. The empirical implementation relies on a unique dataset (about 250,000 patents granted by the EPO), which allows running large-scale sensitivity tests. The results reveal that the new determinants this paper puts forward -filing strategies -are the most stable of all. They also underline strong dependencies of several 'classical' results to the dependent variables and sampling methodologies, partly explaining several inconsistencies observed in the literature.

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Section: Granted Patents Filed 1990-1995 -Robust Standard Errors -Coecontrasting

confidence: 99%

Filing strategies and patent value

Zeebroeck

Potterie

2011

Economics of Innovation and New Technology

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“…In particular, these real options are subject to uncertainty, i.e. the value of the options changes as time evolves and new information about the investment project comes in (Wu, Yen 2007;Wörner, Grupp 2003;Jerak, Wagner 2006;Ernst et al 2010;Verdu et al 2012). Three levels of real option thinking can be distinguished (Faulkner 1996;Wu, Yen 2007;Wörner, Grupp 2003;Dissel et al 2005).…”

Section: Review and Classification Of Technology Valuation Methodsmentioning

confidence: 99%

A Fuzzy Multiple Criteria Comparison of Technology Valuation Methods for the New Materials Development

Cheng

2013

Technological and Economic Development of Economy

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New materials have been recognized as key drivers for corporate profitability and growth in today's fast changing environment. To evaluate the development and gain useful appraising information of new materials thus becomes a critical issue. However, little has been done in discussing the selection of technology valuation methods for the development of new materials. Accordingly, this study adopted the fuzzy AHP method to obtain the opinions of professionals on this issue. These efforts resulted in seven evaluation criteria with one, the "data validity" being given the highest weight, followed by "method adaptability" and "technology development evaluability". It was concluded that the real option approach and income method are the two most applicable technology valuation methods for evaluating new materials development. In addition, the application of the fuzzy AHP method provided a relevant avenue for corporation policy makers and researchers to evaluate the technology valuation methods for new materials.

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“…Among them, for instance: (Wong and Kohn, 1996;Li, 2000;Smith et al, 2000;Kandala et al, 2001;Panagiotelis and Smith, 2008;Ryu et al, 2011) were those who use regression spline with Bayesian approach, while (Lang and Brezger, 2004;Jerak and Wagner, 2006;Nott, 2006;Costa, 2008;Marley and Wand, 2010;Shen, 2011) were those who use p-spline with Bayesian approach. Wang (2011) used a smoothing spline in semiparametric regression model whose parametric components are linear patterned with Bayesian approach.…”

Section: That Comparesmentioning

confidence: 99%

Smoothing Spline in Semiparametric Additive Regression Model With Bayesian Approach

Diana¹,

Budiantara²,

Purhadi³

et al. 2013

Journal of Mathematics and Statistics

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Semiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach cannot be applied for solving the inference problem such as constructing confidence intervals for the regression curve. To construct confidence interval of smoothing spline curve in the semiparametric additive regression model, we propose to use Bayesian approach, by assuming improper Gaussian distribution for prior distribution in nonparametric components and multivariate normal distribution for parametric components. In this study, we obtain parameter estimators for parametric component and smoothing spline estimators for the nonparametric component in semiparametric additive regression model. Moreover, we also develop a smoothing parameters selection method simultaneously using Generalized Maximum Likelihood (GML) and confidence intervals for the parameters of the parametric component and the smoothing spline functions of the nonparametric component using Bayesian approach. By computing each posterior mean and posterior variance of parametric component parameters and smoothing spline functions, confidence intervals can be constructed for the parametric component parameters and confidence interval smoothing spline functions for nonparametric components in semiparametric additive regression models. We create R-code to implement estimation model and inference procedure. Our simulation studies reveal estimation and inference method perform reasonably well.

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Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework

Abstract: Markov Chain Monte Carlo, Bayesian semiparametric binary regression, Bayesian P-splines, Patent opposition,

Cited by 20 publications

References 30 publications

Filing strategies and patent value

Filing strategies and patent value

A Fuzzy Multiple Criteria Comparison of Technology Valuation Methods for the New Materials Development

Smoothing Spline in Semiparametric Additive Regression Model With Bayesian Approach

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