
## Kernel-based estimation for partially  functional linear model: Minimax rates and  randomized sketches
Shaogao Lv, Xin He, Junhui Wang
Keywords: 
JMLR/2023/Proceedings/201461 - Kernel-based estimation for partially  functional linear model: Minimax rates and  randomized sketches.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

Implementation not provided.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[6]

(2/2)

The authors use simulations in section 5.1. The formulae for generation are stated extensively as well as the parameters used. In 5.2 they use National Mortality, Morbidity and Air Pollution study (No citation) with a long description but few statistics. No links or implementations.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[5]

The authors state two hyperparameters in section 3, namely μ and λ and adopt 5-folds cross-validation to select optimal values in 5.1. but not what these were and in 5.2 there are set to zero. No other details.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[2]

The authors replicate each scenario 50 times (No variation, aggregation not specified) and measure estimation accuracy / prediction accuracy (formula given).

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[7]

Requries expertise on kernel-based setimation.
