scholarly journals Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity

2022 ◽  
Author(s):  
Tomohiro Ando ◽  
Jushan Bai
Keyword(s):  
Longitudinal Data ◽  
Large Scale ◽  
Unobserved Heterogeneity ◽  
Data Models

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

scholarly journals The managerial power impact on compensation – some further evidence

2005 ◽  
Vol 2 (3) ◽  
pp. 87-93 ◽  
Author(s):  
Tor Eriksson
Keyword(s):  
Longitudinal Data ◽  
Unobserved Heterogeneity ◽  
Individual Characteristics ◽  
Managerial Compensation ◽  
Compensating Wage Differentials ◽  
Direct Power ◽  
Cross Sectional ◽  
Compensation Model ◽  
Managerial Power ◽  
Alternative Measures

The aim of this paper is to test the managerial power hypothesis more rigorously than in previous studies by: testing it against the compensating wage differentials explanation, using both cross-sectional and longitudinal data, and adopting two alternative measures of managerial power; a frequently used indirect one, and a more direct power indicator. The results of analysis show that despite introducing individual characteristics, when using two or three alternative measures of managerial power and when estimating the managerial compensation model on cross-sectional as well as longitudinal data (the later allowing me to cater for unobserved heterogeneity), the power variables continue to obtain positive and statistically significant co-efficient estimates.

scholarly journals Особливості застосування моделей лонгітюдних даних для сільськогосподарських підприємств

2013 ◽  
pp. 117-120
Author(s):  
Н. О. Окселенко
Keyword(s):  
Longitudinal Data ◽  
Significant Proportion ◽  
Data Models ◽  
Accounts Receivable ◽  
Agricultural Enterprises ◽  
Effective Current ◽  
Economic Interpretation ◽  
Assets Management

Робота розкриває питання вдосконалення процесу управління оборотними активами сільськогосподарських підприємств із використанням моделей лонгітюдних даних. Розроблено систему економетричних ANCOVA-моделей для сільськогосподарських підприємств. Подано економічне тлумачення всіх характеристик зв’язку та показано можливості використання моделей на практи-ці. Значну питому вагу оборотних активів сільськогоспо-дарських підприємств становлять запаси, дебіторська заборгованість, поточні біологічні активи. Доведено, що проблема ефективного управління оборотними актива-ми є водночас і проблемою управління прибутком. The work is devoted to the improvement of the current assets of management of the agricultural enterprises using the longitudinal data models. The system of econometric ANCOVA-models for agricultural enterprises is developed. The economic interpretation of all characteristics of the connection is given and the possibilities of the models use in practice are showed. Significant proportion of current assets of agricultural enterprises constitute reserves, accounts receivable, current biological assets. It was proved that the problem of the effective current assets management is at the same time a problem of profit management.

scholarly journals OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

2021 ◽  
Author(s):  
Theresa A Harbig ◽  
Sabrina Nusrat ◽  
Tali Mazor ◽  
Qianwen Wang ◽  
Alexander Thomson ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Large Scale ◽  
Cancer Genomics ◽  
Temporal Patterns ◽  
Molecular Data ◽  
Liquid Biopsies ◽  
Sequencing Technologies ◽  
Molecular Features ◽  
Wide Range ◽  
The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

2016 ◽  
pp. 133-162 ◽  
Author(s):  
Berkay Aydin ◽  
Vijay Akkineni ◽  
Rafal A Angryk
Keyword(s):  
Data Storage ◽  
Relational Databases ◽  
Large Scale ◽  
Database Systems ◽  
Distributed Database ◽  
Data Models ◽  
Trajectory Data ◽  
Nosql Databases ◽  
Distributed Database Systems ◽  
Partitioned Data

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

Omitted variables in longitudinal data models

2001 ◽  
Vol 29 (4) ◽  
pp. 573-595 ◽  
Author(s):  
Edward W. Frees
Keyword(s):  
Longitudinal Data ◽  
Data Models ◽  
Omitted Variables

scholarly journals A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

2021 ◽  
Author(s):  
Sonal Tuteja ◽  
Rajeev Kumar
Keyword(s):  
Large Scale ◽  
Graph Model ◽  
Heterogeneous Data ◽  
Data Models ◽  
Graph Models ◽  
Query Performance ◽  
Source Models ◽  
Database Size ◽  
Modelling Techniques ◽  
Heterogeneous Source

AbstractThe incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

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