• No results found

Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

N/A
N/A
Protected

Academic year: 2021

Share "Feature network models for proximity data : statistical inference, model selection, network representations and links with related models"

Copied!
2
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Feature network models for proximity data : statistical inference, model selection, network

representations and links with related models

Frank, L.E.

Citation

Frank, L. E. (2006, September 21). Feature network models for proximity data : statistical inference, model selection, network representations and links with related models. Retrieved from https://hdl.handle.net/1887/4560

Version: Not Applicable (or Unknown)

License: Licence agreement concerning inclusion of doctoral thesis in the Institutional Repository of the University of Leiden Downloaded

from: https://hdl.handle.net/1887/4560

(2)

Fe atu re N etw ork M od els fo r P ro xim ity D ata

Laurence E. Frank

La ure nc e E . F ran k

Feature Network Models

for

Proximity Data

p t k f th s sh b d g v dh z dz m n

s

sh

th

f

t

k

p

z

dz

dh

v

d

g

b

n

m

Statistical inference, model selection,

network representations and

Referenties

GERELATEERDE DOCUMENTEN

Subject headings: additive tree; city-block models; distinctive features models; fea- ture models; feature network models; feature selection; Monte Carlo simulation;..

Table 1.3 shows the feature discriminability parameters and the associated theo- retical standard errors and 95% t-confidence intervals for the theoretic features of the plants data.

Table 2.3 shows that the nominal standard errors for both ˆη OLS and ˆη ICLS estimators are almost equal to the empirical variability of these parameters captured by the

Chapter 3 showed that given a special, nested, feature structure, formed by a com- bination of cluster features, unique features and internal nodes, the feature network

H ierna v olgde de studie Franse T aal- en Letterkunde aan de U niv ersiteit Leiden die in 1992 afgesloten werd met h et doctoraal ex amen, en in 1993 werd de eerstegraads- bev

Feature network models for proximity data : statistical inference, model selection, network representations and links with related models..

The results with generalized linear models is compared with an already existing model: Network Enrichment Analysis Test6. The data analysis with generalized linear models gives

Applying the Pearson and Deviance goodness-of-fit tests to a model with two categorical predictors revealed a distinct patter in the statistical power of the test