September 19, 2017 / by / In /

### Supplemental Material: A Study of the Allan Variance for Constant-Mean Non-Stationary Processes

In the paper A Study of the Allan Variance for Constant-Mean Non-Stationary Processes, we provide a new formula to compute the theoretical Allan Variance (AVar) for constant-mean non-stationary processes and discuss its practical implications. This formula is provided for both the Maximum Overlapping AVar (MOAV) and the Non Overlapping AVar (NOAV) and a few examples are given for the former (MOAV) using the following processes

May 23, 2016 / / /

### Process to Haar Wavelet Variance Formulae

The following equations are derivations used within the package as they relate to the Haar Wavelet Variance (WV) theoretical quantities. The initial WV formula, $\nu _j^2$, are used to calculate process to wavelet variance. The later are used within the asymptotic model selection calculations.

February 10, 2016 / by / /

### Eurocow GMWM Workshop 2016

As the lifeforce of our new statistical software package “gmwm” within the R computational environment grows, there is more interest in learning about the estimation methods and software. Most recently, we were honored to be invited to EuroCOW 2016 at École polytechnique fédérale de Lausanne (EPFL) to host a workshop on the Generalized Method of Wavelet Moments (GMWM). These presentations were delivered within two workshop sessions. The first session dealt with exploring the motivation and theory behind the GMWM. The second session acted as a tutorial where features of package related to inertial sensor calibration where displayed. We hope that you find the contents of both sessions to be interesting and/or useful for your research!

February 01, 2016 / by / In /

### Simulation Study using GMWM

The following simulation was designed to mimic the simulation setup in the paper “Maximum Likelihood Identification of Inertial Sensor Noise Model Parameters” of the Generalized Method of Wavelet Moments (GMWM) versus that of the integrated maximum likelihood ($ML_i$). The simulation setup as well as the code used to generate the simulation is contained below to ensure reproducibility.

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