*  Ideal point models - Statistical Modeling, Causal Inference, and Social Science
What behavioral/political 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 ...
  http://andrewgelman.com/2004/11/02/ideal_point_mod/
*  TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling | SpringerLink
... 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, ...
  https://link.springer.com/article/10.3758%2Fs13428-017-0869-7
*  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 ...
  http://www.pnas.org/content/108/7/2825
*  9780805854664 - Intermediate Statistics: A Modern | eCampus.com
Highlights of the Third Edition include: *a new chapter on 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 ...
  http://www.ecampus.com/intermediate-statistics-modern-approach/bk/9780805854664
*  Protocols and Video Articles Authored by Kevin Gaston
... spatial distributions were 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 ...
  https://www.jove.com/author/Kevin_Gaston
*  A Markov Chain Monte Carlo Approach for Joint Inference of Population Structure and Inbreeding Rates From Multilocus Genotype...
The 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 ...
  http://www.genetics.org/content/176/3/1635
*  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 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 ...
  http://www.google.com/patents/US20050220035?dq=6,587,403
*  Past Events (2010) - ANC | Institute for Adaptive and Neural Computation
As opposed to most other sequence 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 ...
  http://www.anc.ed.ac.uk/events/past/2010
*  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 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 ...
  http://www.google.de/patents/US7092748
*  Institut de Recherche en Informatique de Toulouse
... images is then addressed by generalizing this Bayesian framework to 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 ...
  https://www.irit.fr/-Agenda-?evtid=343&evttype=these&lang=en
*  Attribute hierarchy method - Wikipedia
Cognitive 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 ...
  https://en.wikipedia.org/wiki/Attribute_hierarchy_method
*  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, ...
  http://www.pnas.org/content/98/16/8961
*  Determining informative priors for cognitive models | SpringerLink
The development of cognitive 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 ...
  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
*  Multiple hot-deck imputation for network inference from RNA sequencing data, Bioinformatics | 10.1093/bioinformatics/btx819 |...
... log-linear graphical 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 ...
  https://www.deepdyve.com/lp/ou_press/multiple-hot-deck-imputation-for-network-inference-from-rna-sequencing-rtKaPmZf9O
*  Publications by Type: Journal Article | GARY KING
This article also provides an example of a 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 ...
  https://gking.harvard.edu/publications/type/journal-article?page=5
*  Writings | GARY KING
Second, we showed that their 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 ...
  https://gking.harvard.edu/publications?page=8
*  Nonparametric statistics - Wikipedia
non-parametric 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 ...
  https://en.wikipedia.org/wiki/Nonparametric_statistics
*  Hierarchical Linear Models I: Introduction (Amherst, MA)
... checking 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 ...
  https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0063
*  Hierarchical Linear Models: Introduction (Amherst, MA)
... checking 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 ...
  https://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0063?tag=hierarchical&location=Amherst%252C+MA&instructor=Manning%252C+Mark
*  New England Machine Learning Day 2015 - Microsoft Research
Object-based World 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/ ...
  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
*  Bayesian inference and posterior probability maps - UCL Discovery
... under some simple 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 ...
  http://discovery.ucl.ac.uk/65487/
*  Sequence Analysis and Evolutionary Studies of Reelin Proteins
... relies heavily on statistical models that have been extended and refined over the past years into complex hierarchical models ... The method incorporates a birth-death process to model the domain duplications and losses along with a domain sequence ... evolution model with a relaxed molecular clock assumption. The method employs a variant of Markov Chain Monte Carlo technique ... 1. Probabilistic Modelling of Domain and Gene Evolution. Open this publication in new window or tab ,,Probabilistic Modelling ...
  http://kth.diva-portal.org/smash/record.jsf?pid=diva2:897780
*  Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data | Database & Data Warehousing...
3.2.4.1 Filters, Wrappers, Hybrid, and Embedded Models.. 3.2.4.2 Strategy: Exhaustive, Complete, Sequential, Random, and Hybrid ... 3.1.3.2 Terms Associated with Common Assumptions Underlying Parametric Learning Algorithms.. 3.1.3.3 Visualization of ... 2.8.1.3 Hierarchical Clustering.. 2.8.1.4 Two-Way Clustering and Related Methods. ... 3.1.6 Identifying Biological Processes Underlying the Class Differentiation.. 3.2 Feature Selection. ...
  https://www.wiley.com/en-us/Data+Mining+for+Genomics+and+Proteomics%3A+Analysis+of+Gene+and+Protein+Expression+Data+-p-9780470163733
*  Value-added modeling - Wikipedia
Researchers use 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 ...
  https://en.wikipedia.org/wiki/Value-added_modeling
*  Review. How hierarchical is language use? | Proceedings of the Royal Society of London B: Biological Sciences
... ability to model statistical patterns of English was only slightly below that of the hierarchical grammars [52-54]. However, ... based on any set of structural assumptions. Comparisons of RNNs with models that rely on hierarchical structure indicate that ... but a recent computational model explained the phenomenon without relying on any hierarchical processing [68]. ... 4. Towards a non-hierarchical model of language use. In this section, we sketch a model to account for human language behaviour ...
  http://rspb.royalsocietypublishing.org/content/279/1747/4522