arXiv (All)
open access
Phase transitions and approximations of mean squared error for state-space models with fractional differencing
We study trend estimation in state-space models in which the trend has a fractional stochastic difference of order $d>0$ and the observation errors form a short-range-dependent stationary process. Using finite-sequence fractional summation and differencing operators, we analyze the penalized least-s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Identifying the Predictable Drift of a Semimartingale from Marginal Laws#1118084
- Estimation of the First-Order Autoregressive Model with Contaminated Exponential White Noise#274318
- Theory of Statistical Estimation : Contributions to Higher Order Asymptotics and Non-Regular Cases / by Masafumi Akahira#1165539
- Black-Box Assisted Regression: Phase Transitions and Minimax Optimality#307038
- Correct then Forecast: Observer State-Space Models for Time Series Forecasting#1118278
Record · ID 974821
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive