English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Partially linear regression smoothing is a useful technique for modeling time series. Using polynomial splines and a weighted least squares method, this study investigates a class of partially linear regression models of time series with correlated errors.-consistency of parametric estimators and the convergence rate of the nonparametric estimator are derived under some suitable conditions. Simulations reveal that the proposed approach is more valid than that of ignoring correlated errors. Moreover, the importance of considering autoregressive errors is illustrated by making multi-step-ahead forecasts for Australian blow-fly data.

Spline estimation of partially linear regression models for time series with correlated errors