Machine Learning Feature Selection Techniques to Model the Elements of Cash Conversion Cycle and Other Covariates on Hospital Performance | Chapter 4 | Research Updates in Mathematics and Computer Science Vol. 8

 

This study seeks to contribute by empirically modeling the cash conversion cycle on hospital performance using two supervised machine learning feature selection techniques. In data science, model selection instability is a major concern, especially when dealing with a high number of features. Data mining, such as subset selection technique and regularization (shrinkage) techniques, pays attention to how to extract meaningful information by modeling the raw data. We employed methods such as best subset selection coupled with an exhaustive search using linear regression and shrinkage methods (Lasso, Ridge, and ElasticNet) to model a real dataset. The empirical results indicated that the Lasso outperformed the other shrinkage methods in feature selection even though the average root mean squared error (rmse) was close. Again, Account Receivable Days (ARD), Account Payable Days (APD), Inventory Turnover Days (INV), and Debt Ratio were discovered to be predictors of hospital performance, which are also components of the Cash Conversion Cycle. Finally, the results show that, on average, a day decrease in the hospital’s collection period will decrease performance by 1%, and a one-unit increase in a day in the account payable decrease performance by 0.003 times. Future studies could explore more advanced algorithms, like recursive feature elimination selection methods, to enhance the analysis of CCC on hospital performance. Lastly, it is recommended that hospitals focus on restructuring their cash conversion cycle management, particularly concerning days of account payables.

 

Author(s) Details

 

Richmond Essieku

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA and School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley 1201 W University Dr., Edinburg, TX 78541, USA.

Helena Baffoe

Department of Social Work, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

Makafui Komla Akatu

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

Prince Bosompim

School of Financial Planning, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

James Ladzekpo

Department of Economics, Texas Tech University – 2500 Broadway, Lubbock, TX 79409, USA.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/rumcs/v8/440

Two-Stage and One-Stage Subset Selection Procedures for Exponential Populations under Heteroscedasticity | Chapter 6 | Mathematics and Computer Science: Contemporary Developments Vol. 8

This paper investigates subset selection procedures for k (k \(\ge\) 2) independent populations, where each population follows a two-parameter exponential distribution E(\(\mu\)i, \(\theta\)i) with unknown and possibly unequal location \(\mu\)i and scale \(\theta\)i parameters. We define a set of good populations, G = (i \(\mu\)i \(\ge\) \(\mu\)[k] - \(\epsilon\)1) where \(\mu\)[k]  is the maximum location parameter and \(\epsilon\)1 > 0. The goal is to select a subset S of k populations that contains G with a pre-specified probability P*, i.e., P\(\underline{\delta}\) = (G \(\subseteq\) S| under the proposed procedure)\(\ge\) P*\(\forall\)\(\underline{\delta}\)\(\in\)\(\Omega\), where \(\underline{\delta}\) = (\(\mu\)1, ... , \(\mu\)k, \(\theta\)1, ... , \(\theta\)k) \(\in\) Rk X \(R^k_+\) = \(\Omega\). The paper proposes both two-stage and one-stage subset selection procedures and derives simultaneous confidence intervals for the differences in location parameters \(\mu\)[k] - \(\mu\)i, i = 1, ... ,k and [j]-[i],ij=1,. . . ,k. Further, a subset selection procedure is also introduced to control the probability of omitting a "good" population or selecting a "bad" one, defined by B= (i \(\mu\)i \(\le\) \(\mu\)[k] - \(\epsilon\)2), where \(\epsilon\)2 > \(\epsilon\)1, at1 - P* . The implementation of the proposed procedures is demonstrated using real-life data.

 

Author (s) Details

 

Anju Goyal
Department of Statistics, Panjab University, Chandigarh, India.

 

Amar Nath Gill
School of Basic Sciences, IIIT, Una, H.P., India.

 

Vishal Maurya
Department of Statistics and Information Management, RBI, Mumbai, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v8/2054