this study aims to examine how open innovation can be effective in changing organizational inertia to create business model innovation and improve firm performance. it also seeks to explore whether the existence of open innovation has a mediating effect and influence. this study constructs a theoretical model to explore the relationship between latent variables and uses a questionnaire to colle...

The conventional wisdom in macroeconomic modeling is to attribute business cycle fluctuations to innovations in the level of the fundamentals. Though volatility shocks could be important too, their propagating mechanism is still not well understood partly because modeling the latent volatilities can be quite demanding. This paper suggests a simply methodology that can separate the level factors...

Assume individuals are treated if a latent variable, containing a continuous instrument, lies between two thresholds. We place no functional form restrictions on the latent errors. Here unconfoundedness does not hold and identification at infinity is not possible. Yet we still show nonparametric point identification of the average treatment effect. We provide an associated root-n consistent est...

In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. We extend these results by showing that under these assumptions one can estimate...

This paper presents an evaluation method employing a latent variable model for paraphrases with their contexts. We assume that the context of a sentence is indicated by a latent variable of the model as a topic and that the likelihood of each variable can be inferred. A paraphrase is evaluated for whether its sentences are used in the same context. Experimental results showed that the proposed ...

This paper presents a Bayesian latent variable model used to analyze ordinal response survey data by taking into account the characteristics of respondents. The ordinal response data are viewed as multivariate responses arising from continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distributio...

For simplicity we will focus on a simple Gaussian Mixture Model. Consider a mixture of k spherical gaussians in R which is the following generative process. Let w ∈ ∆([k]) denote a distribution and let μ1, . . . , μk ∈ R be the mean vectors. Each point xi is generated by first choosing a component hi ∼ w and then xi ∼ N (μhi , I). We are given n samples x1, . . . , xn drawn according to this pr...

This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This “eigenmodel” generalizes other popular latent variable models, such as latent class and distance...