###### Ideal point **models** - **Statistical** **Modeling**, Causal Inference, and Social Science

What behavioral/political

http://andrewgelman.com/2004/11/02/ideal_point_mod/**assumptions**or**processes**suggest that we ought to do this when we**model**the data? The point here is ... This point is relevant even to something as seemingly innocuous as**hierarchical****modeling**or robust fitting. ... can imply a**statistical****model**. In our paper, we took the**statistical****model**as given and focused on how to fit, display, and ... 2) When a**model**is multidimensional, the number of dimensions is a**modeling**choice. (In our paper, we use 1-dimensional**models**...###### TreeBUGS: An R package for **hierarchical** multinomial-**processing**-tree **modeling** | SpringerLink

...

https://link.springer.com/article/10.3758%2Fs13428-017-0869-7**models**are a class of measurement**models**that account for categorical data by assuming a finite number of underlying cognitive ... Even though this**statistical****assumption**might be appropriate in some cases, the interest is often only in differences of the ... the DIC trades off**model**fit and**model**complexity. After fitting each of the competing**hierarchical**MPT**models**, the**model**with ... Fitting a**hierarchical**beta MPT**model**. The TreeBUGS function betaMPT fits a**hierarchical**beta-MPT**model**(Smith & Batchelder, ...###### Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza | PNAS

**Statistical**Inference.. Assuming that individuals with ARI were infected with the influenza virus, we built a

**statistical**

**model**... 2 shows the

**hierarchical**structure of our Bayesian

**model**. On the right-hand side of the equation, the first term, which is ... 2 corresponds to the "transmission

**model**," which describes the latent transmission

**process**and is characterized by Eq. 1. The ... the observation

**model**relies on the

**assumption**that the incubation period of influenza has a mean of 1.5 d and a variance of ...

###### 9780805854664 - Intermediate Statistics: A Modern | eCampus.com

Highlights of the Third Edition include: *a new chapter on

http://www.ecampus.com/intermediate-statistics-modern-approach/bk/9780805854664**hierarchical**linear**modeling**using HLM6; *a CD containing all of the ... The**assumptions**underlying each analysis are emphasized, and the reader is shown how to test the critical**assumptions**using ... Printouts with annotations from SAS or SPSS show how to**process**the data for each analysis. The annotations highlight what the ... James Stevens' best-selling textis written for those who use, rather than develop,**statistical**techniques. Dr. Stevens focuses ...###### Department of Mathematics and Statistics at UNC Greensboro

Abstract:

http://www.uncg.edu/mat/rmsc/2015/talks-abstracts.html**Statistical****models**for protein alignment typically treat insertion and deletion**processes**at each site along the ... Motivated by problems in multiple alignments of protein structural families, we develop a simple**hierarchical****model**for ... without making additional**assumptions**on the**model**. The existing methods available in the literature for analyzing data from ... a**statistical****model**must be constructed and variable importance computed from it. There are a variety of**statistical****models**and ...###### Protocols and Video Articles Authored by Kevin Gaston

... spatial distributions were

https://www.jove.com/author/Kevin_Gaston**modelled**by a random**hierarchical****process**in which the original 'habitat' patches were randomly ... Criticism of MDE**model****assumptions**does not, however, imply opposition to the use of null**models**in ecology. ... Moreover, the**statistical****process**described may lie behind similar frequency distributions observed in many other scientific ...**Processes**related to priority setting and the development of national red lists need to take account of some**assumptions**in the ...###### A Markov Chain Monte Carlo Approach for Joint Inference of Population Structure and Inbreeding Rates From Multilocus Genotype...

The

http://www.genetics.org/content/176/3/1635**hierarchical**structure of the Dirichlet**process**mixture**model**is. where is the Dirichlet**process**with base distribution F0 ... To relax this**assumption**, we employ the DPMM. The rationale behind this approach is not biological, but**statistical**. Instead of ... That is,**model**1,**model**2, and many combinations in**model**3 had excellent coverage. One exception was**model**3 with sk ∈ {0.05, ... Formally, we think of the Dirichlet**process**mixture**model**as a finite mixture**model**where the number of mixture components is a ...###### Patent US20050220035 - Estimator for end-to-end throughput of wireless networks - Google Patents

The architecture system design of the present invention allows for information gathering independent of the mathematical

http://www.google.com/patents/US20050220035?dq=6,587,403**models**... In a**model**for SNMP operation, a master agent is a**process**running on a managed node to exchange SNMP information, and a ... By using the**statistical**information in conjunction with a timer, the overall network traffic rate at the IGW can be derived ... Known TEs for SNMP rely on the**assumption**that good performance at the server indicates good end-to-end throughput, as the ...###### Past Events (2010) - ANC | Institute for Adaptive and Neural Computation

As opposed to most other sequence

http://www.anc.ed.ac.uk/events/past/2010**models**, our**model**does not make any Markovian**assumptions**. Instead, we use a**hierarchical**... we use a Bayesian nonparametric prior called the Pitman-Yor**process**as building blocks in the**hierarchical****model**. We show state ... Approximating marginals in latent Gaussian**models**In a seminal paper (Journal Royal**Statistical**Society Series B, 2009), Rue, ...**Hierarchical**Bayesian**Models**of Language and Text In this talk I will present a new approach to**modelling**sequence data called ...###### JLee 01122017 | Department of Biomedical Data Science | Stanford Medicine

A robust Bayesian

http://med.stanford.edu/dbds/education/workshop/abstract/JLee_01122017.html**model**is obtained by assuming a Dirichlet**process**for the frailty distribution. The**model**is applied to ... Brown, E. R. and Ibrahim, J. G. (2003). A Bayesian semiparametric joint**hierarchical****model**for longitudinal and survival data. ... is robust to a mild violation of the frailty**model****assumption**, and has favorable estimation reliability and accuracy compared ... Journal of the American**Statistical**Association , just-accepted.. Ghosh, S. K. and Ghosal, S. (2006). Semiparametric ...###### Patent US7092748 - System and method for the tomography of the primary electric current of the ... - Google Patentsuche

The spatial and temporal correlations of the map are

http://www.google.de/patents/US7092748**modeled**as well as their dependence on experimental covariables. The ... From signals, the**statistical**descriptive parameters are obtained and a map of its distribution is calculated. The map is the ... The procedure for the**statistical**classification of subjects is based on the**assumption**that x˜NR 2,p(μx,Σx). This implies that ... The resulting Bayesian**hierarchical****model**is estimated by using the method of "Mean Field Annealing." This**model**, although ...###### Graduate School, Temple University

It is important to focus on the

http://www.temple.edu/gradcourses/archive/2010-2011/cla/gsc_d02428.htm**assumptions**embedded in the**models**and to think about how they might affect what appear to be ... 3. To teach skills of working with**statistical****models**, and to understand how they represent human reality in a simplified way. ... Socialization is the**process**by which individuals become members of society. We look at this**process**as ongoing, starting in ... This course will concentrate on**hierarchical**linear**modeling**. Relying on standard textbooks the instructor will provide ...###### Institut de Recherche en Informatique de Toulouse

... images is then addressed by generalizing this Bayesian framework to

https://www.irit.fr/-Agenda-?evtid=343&evttype=these&lang=en**hierarchical****models**able to account for the**assumption**that ... This**statistical****model**enables us to counterbalance the variability induced by small sample sizes and to embed the estimation ... Texture characterization is a central element in many image**processing**applications. Texture analysis can be embedded in the ... The first limitation is tackled by introducing a generic**statistical****model**for the logarithm of wavelet leaders, parametrized ...###### Attribute hierarchy method - Wikipedia

Cognitive

https://en.wikipedia.org/wiki/Attribute_hierarchy_method**model**development comprises the first stage in the test design**process**. During this stage, the cognitive knowledge, ... This**assumption**better reflects the characteristics of human cognition because cognitive**processes**usually do not work in ... The cognitive**model**can be represented by various**hierarchical**structures. Generally, there are four general forms of ... The purpose of**statistical**pattern recognition is to identify the attribute combinations that the examinee is likely to possess ...###### Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments | PNAS

**Statistical**Framework. The basis of this methodology is a

**statistical**

**model**for microarray data. We use analysis of variance ( ... realizations of a random

**process**. With microarray data, there is no such i.i.d. structure. Instead, we use a structural

**model**... Case 3,

**hierarchical**clustering, is exactly the situation addressed by Felsenstein (7) in his original work on bootstrapping ... In the example, we did not see any evidence in the residuals against our

**assumption**of constant error variance. In principle, ...

###### Determining informative priors for cognitive **models** | SpringerLink

The development of cognitive

https://link.springer.com/article/10.3758/s13423-017-1238-3?wt_mc=Other.Other.8.CON1172.PSBR%20VSI%20Art07%20&%20utm_medium=other%20&%20utm_source=other%20&%20utm_content=2062018%20&%20utm_campaign=8_ago1936_psbr%20vsi%20art07**models**involves the creative scientific formalization of**assumptions**, based on theory, observation ... Cognitive**models**often use likelihoods designed to formalize**assumptions**about psychological**processes**, such as the encoding of ... This involves incorporating additional theoretical**assumptions**into the**model**, and is naturally achieved by**hierarchical**or ... Data analysis typically relies on a standard set of**statistical****models**, especially Generalized Linear**Models**(GLMs) that form ...###### Multiple hot-deck imputation for network inference from RNA sequencing data, Bioinformatics | 10.1093/bioinformatics/btx819 |...

... log-linear graphical

https://www.deepdyve.com/lp/ou_press/multiple-hot-deck-imputation-for-network-inference-from-rna-sequencing-rtKaPmZf9O**model**Allen and Liu (2012) or**hierarchical**Poisson log-normal**model**Gallopin et al. (2013)]. In such ... The large amount of generated data has created a need for multiple bioinformatics and**statistical**post-**processing**of the raw ... and are thus discrete so standard GGM**models**usually used for network inferrence and that are based on Gaussianity**assumption**... log-linear graphical**model**Allen and Liu (2012) or**hierarchical**Poisson log-normal**model**Gallopin et al. (2013)]. In such ...###### Nonparametric statistics - Wikipedia

non-parametric

https://en.wikipedia.org/wiki/Nonparametric_statistics**hierarchical**Bayesian**models**, such as**models**based on the Dirichlet**process**, which allow the number of latent ...**statistical****models**, inference and**statistical**tests. nonparametric statistics (in the sense of a**statistic**over data, which is ... but where nevertheless there may be parametric**assumptions**about the distribution of**model**residuals. ... Non-parametric**models**differ from parametric**models**in that the**model**structure is not specified a priori but is instead ...###### Publications by Type: Journal Article | GARY KING

This article also provides an example of a

https://gking.harvard.edu/publications/type/journal-article?page=5**hierarchical****model**in which the**statistical**idea of "borrowing strength" is used not ... Unfortunately, these**assumptions**are also crucial since changing them leads to different conclusions about voter**processes**. ... Second, we showed that their**statistical****model**includes a slightly hidden but politically implausible**assumption**that a new ... We then specified a**model**without this unrealistic**assumption**and we found that the**assumption**was not supported, and that all ...###### Writings | GARY KING

Second, we showed that their

https://gking.harvard.edu/publications?page=8**statistical****model**includes a slightly hidden but politically implausible**assumption**that a new ... Unfortunately, these**assumptions**are also crucial since changing them leads to different conclusions about voter**processes**. ... items and questions by adding to existing methods of imputation designed for single surveys a**hierarchical**regression**model**... We then specified a**model**without this unrealistic**assumption**and we found that the**assumption**was not supported, and that all ...###### **Hierarchical** Linear **Models** I: Introduction (Amherst, MA)

... checking

https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0063**model****assumptions**, single and multiparameter hypothesis testing, the extension to three-level**models**, and nonlinear ... The course will consider the formulation of**statistical****models**for these three applications. Participants will be exposed to a ... and developmental**processes**. One set of applications focuses on data in which persons are clustered within social contexts, ...**Hierarchical**Linear**Models**I: Introduction (Amherst, MA). Instructor(s):. * Aline Sayer, University of Massachusetts at Amherst ...###### **Hierarchical** Linear **Models**: Introduction (Amherst, MA)

... checking

https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0063?tag=hierarchical&location=Amherst%252C+MA&instructor=Manning%252C+Mark**model****assumptions**, single and multiparameter hypothesis testing, the extension to three-level**models**, and nonlinear ... The course will consider the formulation of**statistical****models**for these three applications. Participants will be exposed to a ... and developmental**processes**. One set of applications focuses on data in which persons are clustered within social contexts, ... The**hierarchical**linear**model**(HLM) provides a conceptual framework and a flexible set of analytic tools to study a variety of ...###### New England Machine Learning Day 2015 - Microsoft Research

Object-based World

https://www.microsoft.com/en-us/research/event/new-england-machine-learning-day-2015/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fevents%2Fneml2015%2F**Modeling**with Dependent Dirichlet**Process**Mixtures. Lawson Wong, MIT CSAIL/Lawson L.S. Wong, Thanard ... I will discuss the state-of-the art**statistical**approaches based on linear mixed**models**for conducting these analyses, in which ... Reliable and scalable variational inference for the**hierarchical**Dirichlet**process**. Michael C. Hughes, Brown University/Michael ... under natural complexity-theoretic**assumptions**. ...**Model**Selection by Linear Programming. Tolga Bolukbasi, BU/ ...###### Value-added **modeling** - Wikipedia

Researchers use

https://en.wikipedia.org/wiki/Value-added_modeling**statistical****processes**on a student's past test scores to predict the student's future test scores, on the ... Statisticians use**hierarchical**linear**modeling**to predict the score for a given student in a given classroom in a given school ...**assumption**that students usually score approximately as well each year as they have in past years. The student's actual score ... The American**Statistical**Association issued an April 8, 2014 statement criticizing the use of value-added**models**in educational ...###### Bayesian inference and posterior probability maps - UCL Discovery

... under some simple

http://discovery.ucl.ac.uk/65487/**assumptions**' about their form. Empirical Bayes requires a**hierarchical**observation**model**, in which higher ... Posterior probability maps (PPMs) represent a complementary alternative to**statistical**parametric maps (SPMs) that are used to ... PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION**PROCESSING**. (pp. 413 - 417). NANYANG TECHNOLOGICAL UNIV ...