- Computing flux upper-limits
for non-detections
- Validating model photometric
uncertainties (priors) empirically
- A method to find variable/transient sources in a stack of image data:

The Consecutive Signed-Slope (CSS) Method (vsn 8/26/2019) - The impact of
correlations on the optimum location (mean) measure and its variance
- Uncertainty and average absolute deviation for two measurements with Gaussian distributed errors
- Cauchy versus Gaussian and
1/√N behavior
- Correlated versus
dependent normally-distributed variables (a paradox?)
- Generating Light Curves from Forced PSF-fit Photometry on ZTF Difference Images (white paper)
- Generating Light Curves from Forced PSF-fit Photometry on PTFIDE Difference-images (white paper)
- Optimal Detection of Rare "sub-significant" Events in the Time-Domain (white paper)
- On the relationship between
linear correlation and slope from a linear regression fit
- A study of the bias from inverse
Poisson-variance weighting
- Optimal image combination with variable seeing and photometric zero-point calibrations
- The danger of statistical inference
in magnitude space
- Quick 'n dirty way to derive the
point-source sensitivity from image data
- Noise-variance (and sigma)
in magnitude space
- Aperture photometry and
uncertainty estimation assuming priors and correlated noise
- Optimum photometric aperture size
for a Gaussian source profile
- Computing the "Number of Noise-Pixels" (HTML version)
- Computing the "Number of Noise-Pixels" (original PDF version)

- Assessing the impact of noisy
pixels on photometric signal-to-noise ratio
- The MADTUT method for robustly
estimating the background noise-sigma
- A pixel-error model for
WISE

(general for any detector using sample-up-the-ramp/non-destructive readout electronics) - Estimating relative pixel responsivities using the gradient method
- Assessing the significance of radial proper motion measurements via the 2-D chi-square
- An application of PCA to a chi-square minimization problem with correlated errors
- Combining Random and Systematic Errors
- Testing and Selecting Nested Models
- Getting started with the "R" statistics programming language