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[PDF] Top 20 Brockhaus, Sarah (2016): Boosting functional regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Has 10000 "Brockhaus, Sarah (2016): Boosting functional regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik" found on our website. Below are the top 20 most common "Brockhaus, Sarah (2016): Boosting functional regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik".

Brockhaus, Sarah
  

(2016):


	Boosting functional regression models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Brockhaus, Sarah (2016): Boosting functional regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... gradient boosting (Mayr et ...chapter. Boosting allows for high-dimensional data settings with more covariate effects than observations and variable selection, but does not provide direct statistical ... See full document

190

Rügamer, David
  

(2018):


	Estimation, model choice and subsequent inference: methods for additive and functional regression models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Rügamer, David (2018): Estimation, model choice and subsequent inference: methods for additive and functional regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... of functional regression as an additive mixed model and has several roots, with some ideas dating back to Hastie and Mallows (1993) as well as Marx and Eilers (1999) and Marx and Eilers (2005), who ... See full document

186

Thiemichen, Stephanie
  

(2016):


	Extensions of exponential random graph models for network data analysis.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Thiemichen, Stephanie (2016): Extensions of exponential random graph models for network data analysis. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... Graph Models to obtain samples consisting of independent observations which allow to use standard generalized linear models (GLM) for model ...additive models (GAM) by adding smooth functional ... See full document

130

Berger, Moritz
  

(2016):


	On the detection of latent structures in categorical data.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Berger, Moritz (2016): On the detection of latent structures in categorical data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... The general concept of recursive partitioning has its roots in automatic interaction detec- tion. The most popular modern version is due to Breiman et al. (1984) and is known by the name classification and ... See full document

256

Tausch, Sarah
  

(2016):


	The influence of computer-mediated feedback on collaboration.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Tausch, Sarah (2016): The influence of computer-mediated feedback on collaboration. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... group models, data on the student’s or groups’ behavior are collected, aggregated and trans- lated into ...These models allow to show statistics on group behavior, for example, on conversation speed or on ... See full document

249

Kriner, Monika
  

(2007):


	Survival Analysis with Multivariate adaptive Regression Splines.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Kriner, Monika (2007): Survival Analysis with Multivariate adaptive Regression Splines. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... worse models in terms of the number of spuriously chosen basis func- ...to models with less basis functions, ...existing functional coher- ences increases with increasing values of ... See full document

162

Belitz, Christiane
  

(2007):


	Model selection in generalised structured additive regression models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Belitz, Christiane (2007): Model selection in generalised structured additive regression models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... Since there are hardly any differences between the two models regarding all other effects, we only show the effects of model (M2). The effects of the categorical covariates are shown in figure 8.15. Many of the ... See full document

237

Reithinger, Florian
  

(2006):


	Mixed models based on likelihood boosting.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Reithinger, Florian (2006): Mixed models based on likelihood boosting. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... by functional relationships. This gives rise to functional data analysis ...neighboring functional objects and thus results in a more representative partitioning of the ... See full document

223

Hofner, Benjamin
  

(2011):


	Boosting in structured additive models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Hofner, Benjamin (2011): Boosting in structured additive models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... In this chapter, we extended the flexible modeling framework based on boosting to allow the inclusion of monotonic or cyclic constraints for certain variables. The monotonicity constraint on continuous variables ... See full document

168

Robinzonov, Nikolay
  

(2013):


	Advances in boosting of temporal and spatial models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Robinzonov, Nikolay (2013): Advances in boosting of temporal and spatial models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... linear models with log-transformed realized volatility as a normalized response and include lagged volatility, financial, and macroeconomic factors as regres- ... See full document

135

Rummel, David
  

(2006):


	Correction for covariate measurement error in nonparametric regression.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Rummel, David (2006): Correction for covariate measurement error in nonparametric regression. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... of functional relationship between independent and depen- dent variables in a regression context is hardly to predict a ...of regression model for the analysis that is as flexible as possible to fit ... See full document

249

Brezger, Andreas
  

(2005):


	Bayesian P-Splines in Structured Additive Regression Models.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Brezger, Andreas (2005): Bayesian P-Splines in Structured Additive Regression Models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... additive models (GAM) for modeling nonlinear effects of contin- uous covariates are now well established tools for the applied ...additive regression (STAR) based on one or two dimensional P-splines as the ... See full document

183

Konrath, Susanne
  

(2013):


	Bayesian regularization in regression models for survival data.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Konrath, Susanne (2013): Bayesian regularization in regression models for survival data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... hazard regression models is already proposed by Hennerfeind et ...survival models is based on the fact that non-linear effects, including also the logarithm of the baseline hazard, can be expressed ... See full document

277

Geistlinger, Ludwig
  

(2016):


	Network-based analysis of gene expression data.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Geistlinger, Ludwig (2016): Network-based analysis of gene expression data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... regulation models, i.e. models in which discrete states of the regulators (TFs and signals) result in discrete quantity states of the regulated gene (for instance a low, medium, or high expression) ... See full document

147

Rauschmayer, Axel
  

(2010):


	Connected Information Management.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Rauschmayer, Axel (2010): Connected Information Management. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... be synchronized between both versions. The web application has multi-user support and authentication. Collaboration is further helped by synchronization, which can be performed between peers. This dissertation ... See full document

307

Helbig, Michael
  

(2009):


	Lifting of Nichols Algebras.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Helbig, Michael (2009): Lifting of Nichols Algebras. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... Zuerst meinem Doktorvater Prof. Dr. Hans-J¨ urgen Schneider f¨ ur seine Unter- st¨ utzung und richtungsweisenden Anregungen in den letzten Jahren. Außerdem Priv.-Doz. Dr. Istv´ an Heckenberger f¨ ur seine stete ... See full document

107

Albrecht, Benjamin
  

(2016):


	Computing hybridization networks using agreement forests.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Albrecht, Benjamin (2016): Computing hybridization networks using agreement forests. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... For that purpose, our approach implements the first non-naive algorithm — called allMulMAAFs — calculating a relevant set of nonbinary maximum acyclic agreement forests for two rooted (n[r] ... See full document

245

Bothmann, Ludwig
  

(2016):


	Efficient statistical analysis of video and image data.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Bothmann, Ludwig (2016): Efficient statistical analysis of video and image data. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... tomatisch und in Echtzeit Fische gezählt und klassifiziert werden können, die von einer Unterwasser-Sonarkamera vor einem Wasserkraftwerk gefilmt wur- ...Anzahl und Art der Fische können Maßnah- men ... See full document

187

Jost, Steffen
  

(2010):


	Automated Amortised Analysis.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Jost, Steffen (2010): Automated Amortised Analysis. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... The two following works discussed are not quantitative resource analyses per se, but they employ dependent types and methods that are relevant to this discussion. Danielsson [Dan08] introduced a library of functions that ... See full document

250

Hang, Alina
  

(2016):


	Exploiting autobiographical memory for fallback authentication on smartphones.


Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Hang, Alina (2016): Exploiting autobiographical memory for fallback authentication on smartphones. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

... I would also like to thank my wonderful colleagues, with whom I have shared so many memorable moments. Alexander De Luca: You have always been there for me throughout my time as a PhD student. You encouraged me to pursue ... See full document

179

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