Panel Data Models with Grouped Factor Structure Under Unknown Group Membership

Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown

This paper proposes a new approach to estimate panel data models with group specific parameters when group membership is not known. We first create a set of “pseudo” threshold variables based on time series estimation of the individual specific parameters. We then use these variables to stratify individuals. The problem of parameter heterogeneity is turned into estimation of a panel threshold m...

Characterizing Unknown Events in MEG Data with Group Factor Analysis

Many current neuroscientific experiments can be seen as data analysis problems with two or more data sources: brain activity and stimulus features or, as in this paper, activity of two brains. These setups have been analyzed with Canonical Correlation Analysis or its multiple-source probabilistic extension Group Factor Analysis, which capture statistical dependencies between the data sources in...

Fitting Models to Grouped Data

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

Latent factor regression models for grouped outcomes.

We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set ...