Random Recursive Partitioning Research Articles

Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques

In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx.

Random Recursive Partitioning: a matching method for the estimation of the average treatment effect

AbstractIn this paper we introduce the Random Recursive Partitioning (RRP) matching method. RRP generates a proximity matrix which might be useful in econometric applications like average treatment effect estimation. RRP is a Monte Carlo method that randomly generates non‐empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all Monte Carlo replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between data sets. The RRP method is “honest” in that it does not match observations “at any cost”: if data sets are separated, the method clearly states it.The match obtained with RRP is invariant under monotonic transformation of the data. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used estimators. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable, in the presence of missing data or when the estimated propensity score is too sensitive to model specifications. Copyright © 2008 John Wiley & Sons, Ltd.

Missing data imputation, matching and other applications of random recursive partitioning

Applications of the random recursive partitioning (RRP) method are described. This method generates a proximity matrix which can be used in non-parametric matching problems such as hot-deck missing data imputation and average treatment effect estimation. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and calculates the proximity between observations as the empirical frequency in the same cell of these random partitions over all the replications. Also, the method in the presence of missing data is invariant under monotonic transformations of the data but no other formal properties of the method are known yet. Therefore, Monte Carlo experiments were conducted in order to explore the performance of the method. A companion software is available as a package for the R statistical environment.

Missing Data Imputation, Classification, Prediction and Average Treatment Effect Estimation Via Random Recursive Partitioning

In this paper we describe some applications of the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in non parametric hot-deck missing data imputation, classification, prediction, average treatment effect estimation and, more generally, in matching problems. RRP is a Monte Carlo procedure that randomly generates non-empty recursive partitions of the data and evaluates the proximity between observations as the empirical frequency they fall in the same cell of these random partitions over all the replications. RRP works also in the presence of missing data and is invariant under monotonic transformations of the data. No other formal properties of the method are known yet, therefore Monte Carlo experiments are provided in order to explore the performance of the method. A companion software is available in the form of a package for the R statistical environment.

Random Recursive Partitioning: A Matching Method for the Estimation of the Average Treatment Effect

In this paper we introduce the Random Recursive Partitioning (RRP) method. This method generates a proximity matrix which can be used in applications like average treatment effect estimation in observational studies. RRP is a Monte Carlo method that randomly generates non-empty recursive partitions of the data and evaluates the proximity between two observations as the empirical frequency they fall in a same cell of these random partitions over all the replications. From the proximity matrix it is possible to derive both graphical and analytical tools to evaluate the extent of the common support between two datasets. The RRP method is honest in that it does not match observations at any cost: if two datasets are separated, the method clearly states it. This method is affine under invariant transformation of the data and hence it is an equal percent bias reduction (EPBR) method when data come from ellipsoidal and symmetric distributions. Average treatment effect estimators derived from the proximity matrix seem to be competitive compared to more commonly used methods (like, e.g., Mahalanobis full match with calipers within propensity scores) even outside the hypotheses leading to EPBR. RRP method does not require a particular structure of the data and for this reason it can be applied when distances like Mahalanobis or Euclidean are not suitable. As a method working on the original data (i.e. on a multidimensional space instead of a one dimensional measure), RRP is affected by the curse of dimensionality when the number of continuous covariates is too high. Asymptotic properties as well as the behaviour of the RRP method under different data distributions are explored using Monte Carlo methods.