Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
First-order marginalised transition random effects models with probit link function
Download
index.pdf
Date
2016-04-03
Author
Asar, Ozgur
İlk Dağ, Özlem
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
134
views
71
downloads
Cite This
Marginalised models, also known as marginally specified models, have recently become a popular tool for analysis of discrete longitudinal data. Despite being a novel statistical methodology, these models introduce complex constraint equations and model fitting algorithms. On the other hand, there is a lack of publicly available software to fit these models. In this paper, we propose a three-level marginalised model for analysis of multivariate longitudinal binary outcome. The implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly. probit link enables direct solutions to the convolution equations. Parameters are estimated by maximum likelihood via a Fisher-Scoring algorithm. A simulation study is conducted to examine the finite-sample properties of the estimator. We illustrate the model with an application to the data set from the Iowa Youth and Families Project. The R package pnmtrem is prepared to fit the model.
Subject Keywords
62H12
,
Maximum likelihood estimation
,
Link functions
,
Statistical software
,
Implicit differentiation
,
Subject-specific inference
,
Correlated data
URI
https://hdl.handle.net/11511/39286
Journal
JOURNAL OF APPLIED STATISTICS
DOI
https://doi.org/10.1080/02664763.2015.1080670
Collections
Department of Statistics, Article
Suggestions
OpenMETU
Core
One-Bit OFDM Receivers via Deep Learning
Balevi, Eren; Andrews, Jeffrey G. (2019-06-01)
This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the fre...
Improved Image Generation in Normalizing Flows through a Multi-Scale Architecture and Variational Training
Sayın, Deniz; Cinbiş, Ramazan Gökberk; Department of Computer Engineering (2022-8-31)
Generative models have been shown to be able to produce very high fidelity samples in natural image generation tasks in recent years, especially using generative adverserial network and denoising diffusion model based approaches. Normalizing flow models are another class of generative models, which are based on learning invertible mappings between the latent space and the image space. Normalizing flow models possess desirable features such as the ability to perform exact density estimation and simple maximu...
A nonlinear model for amplifiers with memory
Yuzer, Ahmet Hayrettin; Demir, Şimşek (2010-12-20)
In this study an improved behavioral modeling including even order terms is proposed to model asymmetric IMD components observed in the multi-tone excitation of nonlinear amplifiers. Only odd order model (OOM) and even order introduced model (EOM), proposed model, are constructed and compared for a sample amplifier. It is showed that introducing even order term is improved the model validity range and accuracy in addition to the decrease model polinomial order. © 2010 IEEE.
A digital neuron realization for the random neural network model
CERKEZ, CUNEYT; AYBAY, HADİ IŞIK; Halıcı, Uğur (1997-06-12)
In this study the neuron of the random neural network (RNN) model (Gelenbe 1989) is designed using digital circuitry. In the RNN model, each neuron accumulates arriving pulses and can fire if its potential at a given instant of time is strictly positive. Firing occurs at random, the intervals between successive firing instants following an exponential distribution of constant rate. When a neuron fires, it routes the generated pulses to the appropriate output lines in accordance with the connection probabili...
A computational model for partially plastic stress analysis of orthotropic variable thickness disks subjected to external pressure
Eraslan, Ahmet Nedim; Yedekçi, Buşra (2014-04-01)
A computational model is developed to predict the states of stressand deformation in partially plastic, orthotropic, variable thickness, nonisothermal, stationary annular disks under external pressure. Assuming a state ofplane stress and using basic equations of mechanics of a disk, Maxwell relation,Hill’s quadratic yield condition, and a Swift type nonlinear hardening law, asingle governing differential equation describing the elastic and partially plasticresponse of an orthotropic, variable thickness, non...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Asar and Ö. İlk Dağ, “First-order marginalised transition random effects models with probit link function,”
JOURNAL OF APPLIED STATISTICS
, pp. 925–942, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39286.