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Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation

Daniel W. Heck, Morten Moshagen, Edgar Erdfelder
2014 Journal of Mathematical Psychology  
Unfortunately, FIA can be misleading in finite samples, resulting in an inversion of the correct rank order of complexity terms for competing models in the worst case.  ...  As a remedy, we propose a lower-bound N' for the sample size that suffices to preclude such errors.  ...  As an example for a rank order inversion of NML complexities in non-nested model families, consider a hierarchical model that assigns a separate set of parameters to each participant.  ... 
doi:10.1016/j.jmp.2014.06.002 fatcat:xxaljfkupvbyvd5koxjtirfdiy

Extension of the matrix Bartlett's formula to the third and fourth order and to noisy linear models with application to parameter estimation

J.-P. Delmas, Y. Meurisse
2005 IEEE Transactions on Signal Processing  
As an application of these extended formulae, we underscore the sensitivity of the asymptotic performance of estimated ARMA parameters by an arbitrary third-or fourth-order-based algorithm with respect  ...  This paper focuses on the extension of the asymptotic covariance of the sample covariance (denoted Bartlett's formula) of linear processes to third-and fourth-order sample cumulant and to noisy linear  ...  As an application of these closed-form expressions, the sensitivity of the asymptotic performance of the estimated ARMA parameters by an arbitrary third-or fourth-order-based algorithm to the SNR, the  ... 
doi:10.1109/tsp.2005.850362 fatcat:qtcpxtv5ijhx5j4dui4z4sx4vi

Model Selection in Continuous Test Norming With GAMLSS

Lieke Voncken, Casper J. Albers, Marieke E. Timmerman
2017 Assessment (Odessa, Fla.)  
Akaike information criterion (3) , cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design.  ...  The advocated model selection procedure is illustrated with norming data of an intelligence test.  ...  Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.  ... 
doi:10.1177/1073191117715113 pmid:28662589 fatcat:o5qgu2phbbgunks34vo4c2oq7e

Blind Separation Of Complex-Valued Signals By Real-Valued In-Phase And Quadrature Rotations

Ira Clarke
2015 Zenodo  
Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000  ...  REAL-VALUED ICA The basic objective in ICA algorithms [1, 2] is to find the 'most likely' set of 'independent' output waveforms as a function of estimated parameters of an unmixing transform that is  ...  These require the summation of sample by sample products of four signal terms such as cccc ∑ xyzz where the summation is over a finite number of samples.  ... 
doi:10.5281/zenodo.37404 fatcat:hjun4gesiffcxnazkjrq46fmsu

Modified AIC and MDL Model Selection Criteria for Short Data Records

F. DeRidder, R. Pintelon, J. Schoukens, D.P. Gillikin
2005 IEEE Transactions on Instrumentation and Measurement  
the number of estimated model parameters.  ...  PROBLEM STATEMENT A N IDENTIFICATION procedure typically consists of estimating the parameters of different models and then selecting the optimal model complexity within that set.  ...  With an increasing number of samples the new rules (7) and (8) converges asymptotically to the old rules (1) and (2) . So, the new rules can be used for short as well as for long data records.  ... 
doi:10.1109/tim.2004.838132 fatcat:dn5vpqdutfcu7ki3hun4kyswve

DESIGN AND IMPLEMENTATION OF DIGITAL FILTER USING CIC AND FIR FILTER

Shweta Madiwalar .
2017 International Journal of Research in Engineering and Technology  
Hence several comparisons made using CIC-FIR Structure for different order and sampling frequency to prove that CIC-FIR structure is better to design BPF.  ...  This structure will reduce the sampling rate of the filter making system requirements feasible to implement.  ...  The design parameters are as tabulated in Table 4 and Table 5 .From these two different design parameter we can observe that sampling rate is reduced and also the order of the filter.  ... 
doi:10.15623/ijret.2017.0601015 fatcat:ucf237wvcne3pcfwovh6gh52eu

A Review of Model Order Reduction Methods for Large-Scale Structure Systems

Kuan Lu, Kangyu Zhang, Haopeng Zhang, Xiaohui Gu, Yulin Jin, Shibo Zhao, Chao Fu, Yongfeng Yang, Zeqi Lu
2021 Shock and Vibration  
Finally, the outlooks of model order reduction of high-dimensional complex systems are provided for future work.  ...  POD is classified into two categories in terms of the sampling and the parameter robustness, and the research progresses in the recent years are presented to the domestic researchers for the study and  ...  What conditions can the POD reduced-order modes reduce the parameter domain or is there an optimal POD reduced-order mode for the entire parameter domain?  ... 
doi:10.1155/2021/6631180 fatcat:4mcopkpjvbe5xaufose2teid6q

Robust learning of inhomogeneous PMMs

Ralf Eggeling, Teemu Roos, Petri Myllymäki, Ivo Grosse
2014 International Conference on Artificial Intelligence and Statistics  
to more complex settings than before.  ...  Structure and parameter learning of these models has been proposed using a Bayesian approach, which entails the practically challenging choice of the prior distribution.  ...  Stiftungsfonds der MLU Halle-Wittenberg and the Academy of Finland (Centre of Excellence COIN and Project PRIME).  ... 
dblp:conf/aistats/EggelingRMG14 fatcat:q455nndsnjfapjhp7gxdlk6hdm

Open-Loop Digital Predistorter for RF Power Amplifiers Using Dynamic Deviation Reduction-Based Volterra Series

Anding Zhu, P.J. Draxler, J.J. Yan, T.J. Brazil, D.F. Kimball, P.M. Asbeck
2008 IEEE transactions on microwave theory and techniques  
It is shown that a further reduction in system complexity can be achieved by applying under-sampling theory in the model extraction and utilizing parameter interpolation in the DPD implementation.  ...  In this approach, the parameters of the predistorter can be directly extracted from an offline system identification process.  ...  To overcome the complexity of the general Volterra series, an effective model-order reduction method, called dynamic deviation reduction, was proposed in [13] .  ... 
doi:10.1109/tmtt.2008.925211 fatcat:x3xmv3kkrzgcdaqqfv22miuz6a

Joint Model Order Selection and Parameter Estimation of Chirps With Harmonic Components

Yaron Doweck, Alon Amar, Israel Cohen
2015 IEEE Transactions on Signal Processing  
samples of the observed signal.  ...  To avoid an exhaustive search in the initial frequency-frequency rate space involved by those estimators, we propose an alternative low-complexity two-step estimation method.  ...  Next, we demonstrate the model order selection and the parameter estimation of a recording of an echolocation call produced by a G. melas whale [60] . The signal is sampled to 44.1 kHz.  ... 
doi:10.1109/tsp.2015.2391075 fatcat:voc6f2gxxrfptbdv67yqk7fprm

A machine learning approach to distribution identification in non-Gaussian clutter

Justin Metcalf, Shannon Blunt, Braham Himed
2014 2014 IEEE Radar Conference  
We consider a set of non-linear transformations of order statistics incorporated into a machine learning approach to perform distribution identification from data with low sample support with the ultimate  ...  The set of transformations provide a means with which data may be compared to a library of known clutter distributions.  ...  The samples of q are then ordered, forming the order statistics q (1) ≤ q (2) ≤ · · · ≤ q (N ) . (4) Finally, the studentized order statistics of y are defined as z (i) = q (i) −q σ . (5) Studentization  ... 
doi:10.1109/radar.2014.6875688 fatcat:4pci4vjlgvemrkpziv3ic5zyde

Model complexity control for hydrologic prediction

G. Schoups, N. C. van de Giesen, H. H. G. Savenije
2008 Water Resources Research  
The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension.  ...  Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases  ...  As an example, consider a first-order polynomial with two parameters, slope and intercept. This model can be fitted exactly through any two, but not three, data points.  ... 
doi:10.1029/2008wr006836 fatcat:psrqspzhdbdvhfaszkccgdq5iy

Fast and accurate algorithm for the computation of complex linear canonical transforms

Aykut Koç, Haldun M. Ozaktas, Lambertus Hesselink
2010 Optical Society of America. Journal A: Optics, Image Science, and Vision  
Complex-ordered fractional Fourier transforms (CFRTs) are a special case of CLCTs, and therefore a fast and accurate algorithm to compute CFRTs is included as a special case of the presented algorithm.  ...  The algorithm is based on decomposition of an arbitrary CLCT matrix into real and complex chirp multiplications and Fourier transforms.  ...  complex FRT in the sense of the order parameter being a complex number.  ... 
doi:10.1364/josaa.27.001896 pmid:20808396 fatcat:mk4fnsari5bbflxxabmhcb7rcq

Transition to collective oscillations in finite Kuramoto ensembles

Franziska Peter, Arkady Pikovsky
2018 Physical review. E  
For this purpose, the minimal value of the amplitude of the complex Kuramoto order parameter appears as a proper indicator.  ...  We prove this by integrating a self-consistency equation for the complex Kuramoto order parameter for two families of distributions with controlled kurtosis and skewness, respectively.  ...  ACKNOWLEDGMENTS This paper was developed within the scope of the IRTG 1740/TRP 2015/50122-0, funded by the DFG/ FAPESP. In studies presented in Sec.  ... 
doi:10.1103/physreve.97.032310 pmid:29776135 fatcat:bn6gpll4vfa53hkubob4omtdiy

Automated Fitting and Rational Modeling Algorithm for EM-Based S-Parameter Data [chapter]

Tom Dhaene
2002 Lecture Notes in Computer Science  
The adaptive algorithm doesn't require any a priori knowledge of the dynamics of the system to select an appropriate sample distribution and an appropriate model complexity.  ...  A smart adaptive algorithm is presented to model the spectral response of general passive planar electrical structures over a frequency range of interest, based on a limited number of data samples.  ...  Traditionally, some a priori knowledge of the dynamics of the circuit parameters is required in order to select an appropriate sample distribution and an appropriate model complexity to represent the spectral  ... 
doi:10.1007/3-540-48051-x_11 fatcat:2ui7tziyjbeipg3kxwiqdtp3im
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