People commonly believe that soft toys exist only for children because they fill nursery shelves with colourful bears, and children use plush rabbits during their bedtime routines before bringing their stuffed companions to school for comfort. A hidden truth remains unacknowledged when people make this common assumption. A significant number of adults maintain their soft […] The post The Psychology Behind Soft Toys Attachment appeared first on Psychologs Magazine | Mental Health Magazine ...| Psychologs Magazine | Mental Health Magazine | Psychology Magazine | Self-Hel...
Background The goal here is strong internal validation after fitting a pre-specified regression model or one that was derived using backwards step-down variable selection such that the same variable selection procedure can be repeated afresh for each bootstrap repetition. So strong internal validation means estimating a variety of model performance measures in a way that does not reward them for overfitting and that penalizes for all aspects of model selection and derivation that utilized the...| Statistical Thinking
Crazy busy with Crime De-Coder and day job, so this blog has gone by the wayside for a bit. I am doing more python training for crime analysts, most recently in Austin. If you want to get a flavor of the training, I have posted a few example videos on YouTube. Here is an example […]| Andrew Wheeler
Benchmarking complex systems can be difficult. Here's a problem I'm currently investigating, possibly related to process scheduling.| Tomas Vondra
Figure 1 An interesting Bayesian functional regression trick based on the so-called -exponential distribution: which has a special relationship with Besov spaces and so connects to functional inverse problems. It seems to be in the same family as elliptical process as per Bånkestad et al. (2020). NB, the -exponential distribution is not the Tsallis q-exponential distribution but rather one developed by Dashti, Harris, and Stuart (n.d.). Li, O’Connor, and Lan (2023): Regularization is one ...| The Dan MacKinlay stable of variably-well-consider’d enterprises
Background This article considers the following setting. Suppose we have one continuous predictor and an outcome variable and we wish to estimate a smooth, usually nonlinear, relationship between and some property of such as the mean or the probability that exceeds some specified value. When there is no censoring on , one can estimate such a smooth relationship nonparametrically using a standard smoother such as loess or the R “super smoother” supsmu. Semiparametric ordinal regression, us...| Statistical Thinking
Overview Maximum likelihood estimation (MLE) is a gold standard estimation procedure in non-Bayesian statistics, and the likelihood function is central to Bayesian statistics (even though it is not maximized in the Bayesian paradigm). MLE may be unpenalized (the standard approach) or various penalty functions such as L1 (lasso, absolute value penalty), and L2 (ridge regression; quadratic) penalties may be added to the log-likelihood to achieve shrinkage (aka regularization). I have been doing...| Statistical Thinking
Event: Consilium Scientific Slides Video| Statistical Thinking
Event: International Chinese Statistical Association Applied Statistics Symposium, Nashville, Tennessee USA Slides| Statistical Thinking
Isotonic regression (also called monotonic regression) is a type of regression model that assumes that the response variable is a monotonic function of the explanatory variable(s).| The DO Loop
A SAS analyst read my previous article about visualizing the predicted values for a regression model that uses spline effects.| The DO Loop
Background A binary endpoint in a clinical trial is a minimum-information endpoint that yields the lowest power for treatment comparisons. A time-to-event outcome, when only a minority of subjects suffer the event, has little power gain over a pure binary endpoint, since its power comes from the number of events (number of uncensored observations). The highest power endpoint would be from a continuous variable that is measured precisely and reflects the clinical outcome situation. An ordinal ...| Statistical Thinking
Background The log-rank test is a Mantel-Haenszel “observed - expected frequency” type of test that was derived in a slightly ad hoc way by Nathan Mantel in 1966 and named the logrank test by R Peto and J Peto in 1972. It was later formally derived as the rank test having optimal local power for a shift in the type I extreme value (Gumbel) distribution. This horizontal shift is equivalent to a vertical shift in survival distributions after log-log transforming them. This is identical to s...| Statistical Thinking
A Definition All statistical procedures have assumptions. Even the most simple response variable (Y) where the possible values are 0 and 1, when analyzed using the proportion that Y=1, assumes that Y is truly binary, every observation has the same probability that Y=1, and that observations are independent. Non-categorical Y have more assumptions. Even simple descriptive statistics have assumptions as described below. But what does it mean that an assumption is required for using a statistica...| Statistical Thinking
UCLA Cardiology Grand Rounds 2020-10-23 | Video (better video below) Vanderbilt University Department of Biostatistics 2020-11-18 Vanderbilt Translational Research Forum 2021-11-04 | Video Consilium Scientific 2024-03-14 | Video and here Slides| Statistical Thinking
Slides Elaborations Video| Statistical Thinking
A quick introduction to using ERT (Emacs Lisp Regression Testing) for| A Scripter's Notes
In my 100th Tech 101 blog post, I am writing about a topic that I am yet to cover — Machine Learning. With the field now fairly mature and plenty of programming languages and companies supporting it with excellent software, it is high time for me to educate my readers on what exactly machine learning … Continue reading "Machine Learning: A primer" The post Machine Learning: A primer appeared first on Tech 101.| Tech 101
