Bias-Variance Trade-Off in Continuous Test Norming

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Bias-variance trade-off for prequential model list selection

The prequential approach to statistics leads naturally to model list selection because the sequential reformulation of the problem is a guided search over model lists drawn from a model space. That is, continually updating the action space of a decision problem to achieve optimal prediction forces the collection of models under consideration to grow neither too fast nor too slow to avoid excess...

متن کامل

Model Selection in Continuous Test Norming With GAMLSS.

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it i...

متن کامل

Bias-Variance Trade-offs: Novel Applications

Consider a given random variable F and a random variable that we can modify, F̂ . We wish to use a sample of F̂ as an estimate of a sample of F . The mean squared error between such a pair of samples is a sum of four terms. The first term reflects the statistical coupling between F and F̂ and is conventionally ignored in bias-variance analysis. The second term reflects the inherent noise in F and ...

متن کامل

Least-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems

In the context of large space MDPs with linear value function approximation, we introduce a new approximate version of λ-Policy Iteration (Bertsekas & Ioffe, 1996), a method that generalizes Value Iteration and Policy Iteration with a parameter λ ∈ (0, 1). Our approach, called Least-Squares λ Policy Iteration, generalizes LSPI (Lagoudakis & Parr, 2003) which makes efficient use of training samp...

متن کامل

The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles

By performing experiments on publicly available multi-class datasets we examine the effect of bootstrapping on the bias/variance behaviour of error-correcting output code ensembles. We present evidence to show that the general trend is for bootstrapping to reduce variance but to slightly increase bias error. This generally leads to an improvement in the lowest attainable ensemble error, however...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Assessment

سال: 2020

ISSN: 1073-1911,1552-3489

DOI: 10.1177/1073191120939155