We study the least-squares estimator in the scalar autoregressive model of order 1 with Gaussian noise and arbitrary ÿxed initial state. Upper bounds of both large and moderate deviations principles are achieved in the unstable and explosive frameworks. The moderate deviations results are consistent
✦ LIBER ✦
Moderate deviations for longest increasing subsequences: The upper tail
✍ Scribed by Matthias Löwe; Franz Merkl
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 212 KB
- Volume
- 54
- Category
- Article
- ISSN
- 0010-3640
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We present a simple, novel and efficient algorithm for the determination of a longest increasing subsequence in a given sequence of ,, numbers. Our algorithm performs in O(,~ log r) time in the worst case, where r is the size of the output, i.e. r is the length of the longest increasing subsequence