scholarly journals Wang and Leng (2016), High‐dimensional ordinary least‐squares projection for screening variables, Journal of the Royal Statistical Society Series B, 78, 589–611

2021 ◽  
Author(s):  
Xiangyu Wang ◽  
Chenlei Leng ◽  
Tom Boot
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
High Dimensional ◽  
Royal Statistical Society

Lasso-based simulation for high-dimensional multi-period portfolio optimization

2019 ◽  
Vol 31 (3) ◽  
pp. 257-280
Author(s):  
Zhongyu Li ◽  
Ka Ho Tsang ◽  
Hoi Ying Wong
Keyword(s):  
Least Squares ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Estimation Error ◽  
Sample Path ◽  
Ordinary Least Squares ◽  
Monte Carlo Algorithm ◽  
High Dimensional ◽  
Finite Sample ◽  
Sample Paths

Abstract This paper proposes a regression-based simulation algorithm for multi-period mean-variance portfolio optimization problems with constraints under a high-dimensional setting. For a high-dimensional portfolio, the least squares Monte Carlo algorithm for portfolio optimization can perform less satisfactorily with finite sample paths due to the estimation error from the ordinary least squares (OLS) in the regression steps. Our algorithm, which resolves this problem e, that demonstrates significant improvements in numerical performance for the case of finite sample path and high dimensionality. Specifically, we replace the OLS by the least absolute shrinkage and selection operator (lasso). Our major contribution is the proof of the asymptotic convergence of the novel lasso-based simulation in a recursive regression setting. Numerical experiments suggest that our algorithm achieves good stability in both low- and higher-dimensional cases.

scholarly journals HB-PLS: An algorithm for identifying biological process or pathway regulators by integrating Huber loss and Berhu penalty with partial least squares regression

2020 ◽  
Author(s):  
Wenping Deng ◽  
Kui Zhang ◽  
Zhigang Wei ◽  
Lihu Wang ◽  
Cheng He ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Regulatory Networks ◽  
Biological Process ◽  
Ordinary Least Squares ◽  
Descent Method ◽  
High Dimensional ◽  
Gradient Descent Method ◽  
Least Squares Regression ◽  
Non Gaussian

AbstractGene expression data features high dimensionality, multicollinearity, and the existence of outlier or non-Gaussian distribution noise, which make the identification of true regulatory genes controlling a biological process or pathway difficult. In this study, we embedded the Huber-Berhu (HB) regression into the partial least squares (PLS) framework and created a new method called HB-PLS for predicting biological process or pathway regulators through construction of regulatory networks. PLS is an alternative to ordinary least squares (OLS) for handling multicollinearity in high dimensional data. The Huber loss is more robust to outliers than square loss, and the Berhu penalty can obtain a better balance between the ℓ2 penalty and the ℓ1 penalty. HB-PLS therefore inherits the advantages of the Huber loss, the Berhu penalty, and PLS. To solve the Huber-Berhu regression, a fast proximal gradient descent method was developed; the HB regression runs much faster than CVX, a Matlab-based modeling system for convex optimization. Implementation of HB-PLS to real transcriptomic data from Arabidopsis and maize led to the identification of many pathway regulators that had previously been identified experimentally. In terms of its efficiency in identifying positive biological process or pathway regulators, HB-PLS is comparable to sparse partial least squares (SPLS), a very efficient method developed for variable selection and dimension reduction in handling multicollinearity in high dimensional genomic data. However, HB-PLS is able to identify some distinct regulators, and in one case identify more positive regulators at the top of output list, which can reduce the burden for experimental test of the identified candidate targets. Our study suggests that HB-PLS is instrumental for identifying biological process and pathway genes.

High-Dimensional Least-Squares with Perfect Positive Correlation

2019 ◽  
Vol 36 (04) ◽  
pp. 1950016
Author(s):  
Zhiyong Huang ◽  
Ziyan Luo ◽  
Naihua Xiu
Keyword(s):  
Least Squares ◽  
Correlation Coefficient ◽  
Large Scale ◽  
Rank Correlation ◽  
Optimal Solution ◽  
Ordinary Least Squares ◽  
High Dimensional ◽  
Dual Algorithm ◽  
Positive Correlation ◽  
Proposed Model

The least-squares is a common and important method in linear regression. However, it often leads to overfitting phenomenon as dealing with high-dimensional problems, and various regularization schemes regarding prior information for specific problems are studied to make up such a deficiency. In the sense of Kendall’s [Formula: see text] from the community of nonparametric analysis, we establish a new model wherein the ordinary least-squares is equipped with perfect positive correlation constraint, sought to maintain the concordance of the rankings of the observations and the systematic components. By sorting the observations into an ascending order, we reduce the perfect positive correlation constraint into a linear inequality system. The resulting linearly constrained least-squares problem together with its dual problem is shown to be solvable. In particular, we introduce a mild assumption on the observations and the measurement matrix which rules out the zero vector from the optimal solution set. This indicates that our proposed model is statistically meaningful. To handle large-scale instances, we propose an efficient alternating direction method of multipliers (ADMM) to solve the proposed model from the dual perspective. The effectiveness of our model compared to ordinary least-squares is evaluated in terms of rank correlation coefficient between outputs and the systematic components, and the efficiency of our dual algorithm is demonstrated with the comparison to three efficient solvers via CVX in terms of computation time, solution accuracy and rank correlation coefficient.

PENGARUH STRUKTUR KEPEMILIKAN BANK TERHADAP KINERJA UNIT USAHA SYARIAH PADA BANK DI INDONESIA

2018 ◽  
Vol 1 (2) ◽  
pp. 25-32
Author(s):  
Hafidzah Nurjannah ◽  
Yul Efnita ◽  
Eva Sundari
Keyword(s):  
Least Squares ◽  
Firm Size ◽  
Ordinary Least Squares

Penelitian ini bertujuan untuk menganalisa pengaruh secara signifikan baik partial maupun simultan pada variabel kepemilikan bank, simpanan (DPK), rasio pinjaman terhadap simpanan (LDR) dan rasio kecukupan modal (CAR), Non Performing Loan (NPL) dan ukuran perusahaan terhadap profitabilitas (ROA) pada bank yang memiliki Unit Usaha Syariah (UUS) baik itu pada Bank Pembangunan Daerah (BPD) maupun Bank Swasta. Populasi dan sampel terdiri dari 24 UUS milik Bank Pembangunan Daerah (BPD) dan Bank Swasta. Dari ke 24 bank tersebut, hanya 18 bank yang dipilih menjadi sampel. Bank-bank tersebut adalah 7 Bank Swasta dan 11 Bank Pembangunan Daerah. Periode penelitian ini adalah 2010-2014. Data diambil dari laporan tahunan bank. Penelitian ini menggunakan data panel dan pooled Ordinary Least Squares (OLS). Hasil penelitian menunjukkan UUS milik Bank Pembangunan Daerah lebih baik daripada Bank Swasta. Hal ini disebabkan beberapa faktor. Pertama, pinjaman hanya untuk pejabat pemerintah daerah di mana pembayaran pinjaman melalui pengurangan gaji, sehingga kemungkinan tidak dapat membayar pinjaman sangat rendah meskipun situasi ekonomi tidak stabil. Kedua, karena Bank Pembangunan Daerah menyediakan layanan hanya untuk lokal saja, sehingga memiliki pengetahuan khusus tentang daerah tersebut. Sehingga akan memungkinkan nasabah menilai penerapan pinjaman dan mengidentifikasi pinjaman yang memenuhi syarat. Ketiga, kinerja Bank Pembangunan Daerah yang diawasi oleh pemerintah daerah lebih intensif. Kata Kunci : Hedging, Laverage, Cash Ratio, Firm Size, Bank Syariah.

The antecedents of corporate social and environmental responsibility discourse in Pakistan: Multiple theoretical perspectives

2019 ◽  
Author(s):  
Muhammad Farhan Basheer ◽  
Saqib Muneer ◽  
Muhammad Atif ◽  
Zubair Ahmad
Keyword(s):  
Institutional Theory ◽  
Least Squares ◽  
Agency Theory ◽  
Stakeholder Theory ◽  
Fixed Effects ◽  
Ordinary Least Squares ◽  
Environmental Responsibility ◽  
Legitimacy Theory ◽  
Discourse Practices ◽  
Corporate Social

The primary purpose of the study is to explore the antecedents of corporate social and environmental responsibilities discourse practices in Pakistan. The industry sensitivity, government shareholding, block holder ownership, print media coverage, environmental monitoring programs, and strategic posture are examined as antecedents of corporate social and environmental responsibility practices. A multidimensional theoretical perspective namely stakeholder theory (ST), institutional theory (IT), agency theory (PAT), and legitimacy theory (LT) is used to conceptualize the phenomena. All the four of perspective theories (positive accounting theory, legitimacy theory, stakeholder theory, and institutional theory) claim that there are ‘pressures’ that impact the organization. How much ‘pressures’ are recognized, managed or satisfied differs from one perspective of theory to the other. To estimate the data, this study uses three sets of panel data models, i.e., the pooled ordinary least squares model (POLS) or constant coefficients model, fixed effects (FEM or least squares dummy variable/LSDV model) and random-effects models. The final sample is comprising of 173 firms over eight years from 2011 to 2017. The firms listed in PSX are included in the sample. Overall the findings of the study have shown agreement with the proposed results. However, the study has provided more support to the institutional theory and stakeholder theory. Keywords: Corporate Social Responsibility, Stakeholders Theory, Agency Theory, Pakistan

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