scholarly journals L1 word order and sensitivity to verb bias in L2 processing – CORRIGENDUM

2013 ◽  
Vol 17 (1) ◽  
pp. 234-236
Author(s):  
EUN-KYUNG LEE ◽  
DORA HSIN-YI LU ◽  
SUSAN M. GARNSEY
Keyword(s):  
Word Order ◽  
Control Variables ◽  
L2 Processing ◽  
Verb Bias ◽  
And Control

Predictors and control variables were not distinguished in Tables 4–8 of the paper by Lee, Lu and Garnsey (2013). The corrected tables are provided below. The authors apologize for these errors.

L1 word order and sensitivity to verb bias in L2 processing

2013 ◽  
Vol 16 (4) ◽  
pp. 761-775 ◽  
Author(s):  
EUN-KYUNG LEE ◽  
DORA HSIN-YI LU ◽  
SUSAN M. GARNSEY
Keyword(s):  
Word Order ◽  
Sentence Comprehension ◽  
Native English Speakers ◽  
Target Language ◽  
English Speakers ◽  
Sentence Structure ◽  
L2 Processing ◽  
Verb Bias ◽  
On Line ◽  
Korean Learners

Using a self-paced reading task, this study examines whether second language (L2) learners are flexible enough to learn L2 parsing strategies that are not useful in their first language (L1). Native Korean-speaking learners of English were compared with native English speakers on resolving a temporary ambiguity about the relationship between a verb and the noun following it (e.g.,The student read [that] the article. . .). Consistent with previous studies, native English reading times showed the usual interaction between the optional complementizerthatand the particular verb's bias about the structures that can follow it. Lower proficiency L1-Korean learners of L2-English did not show a similar interaction, but higher proficiency learners did. Thus, despite native language word order differences (English: SVO; Korean: SOV) that determine the availability of verbs early enough in sentences to generate predictions about upcoming sentence structure, higher proficiency L1-Korean learners were able to learn to optimally combine verb bias and complementizer cues on-line during sentence comprehension just as native English speakers did, while lower proficiency learners had not yet learned to do so. Optimal interactive cue combination during L2 sentence comprehension can probably be achieved only after sufficient experience with the target language.

scholarly journals Optimal Control of an SIR Model with Delay in State and Control Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohamed Elhia ◽  
Mostafa Rachik ◽  
Elhabib Benlahmar
Keyword(s):  
Optimal Control ◽  
Influenza A ◽  
Optimization Strategy ◽  
Sir Epidemic Model ◽  
Optimal Control Strategy ◽  
Control Variables ◽  
Sir Epidemic ◽  
Influenza A H1n1 ◽  
Using Data ◽  
And Control

We will investigate the optimal control strategy of an SIR epidemic model with time delay in state and control variables. We use a vaccination program to minimize the number of susceptible and infected individuals and to maximize the number of recovered individuals. Existence for the optimal control is established; Pontryagin’s maximum principle is used to characterize this optimal control, and the optimality system is solved by a discretization method based on the forward and backward difference approximations. The numerical simulation is carried out using data regarding the course of influenza A (H1N1) in Morocco. The obtained results confirm the performance of the optimization strategy.

scholarly journals Leverage Keuangan Dan Likuiditas Saham Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Jakarta

2008 ◽  
Vol 1 (3) ◽  
Author(s):  
I Made Sudana ◽  
Nurul Intan
Keyword(s):  
Multivariate Regression ◽  
Institutional Ownership ◽  
Financial Leverage ◽  
Return On Assets ◽  
Control Variables ◽  
Stock Liquidity ◽  
Market Capitalization ◽  
Risk Return ◽  
Independent Variable ◽  
And Control

This research focus on the effect of financial leverage on stock liquidity, with control variables included stock risk, return on assets, market capitalization, volume, institutional ownership during 2003-2004. This research used 86 manufacture company that listing in Jakarta Stock Exchange. Analysis method use multivariate regression. The result of partial multivariate regression indicates that financial leverage as independent variable and control variables include stock risk, return on assets, market capitalization, volume, institutional ownership have positive impact on stock liquidity. Independent variable is financial leverage and control variables include return on assets, volume, institutional ownership have significant impact on stock liquidity, while other control variables such as stock risk and market capitalization have not significant impact on stock liquidity. Simultaneously, financial leverage, stock risk, return on assets, market capitalization, volume, institutional ownership have significant impact on stock liquidity. 

An efficient approximate method for solving two-dimensional fractional optimal control problems using generalized fractional order of Bernstein functions

2020 ◽  
Author(s):  
Ali Ketabdari ◽  
Mohammad Hadi Farahi ◽  
Sohrab Effati
Keyword(s):  
Optimal Control ◽  
Fractional Order ◽  
Operational Matrix ◽  
Two Dimensional ◽  
Algebraic Equations ◽  
Control Variables ◽  
Fractional Optimal Control ◽  
Bernstein Functions ◽  
Fractional Optimal Control Problems ◽  
And Control

Abstract We define a new operational matrix of fractional derivative in the Caputo type and apply a spectral method to solve a two-dimensional fractional optimal control problem (2D-FOCP). To acquire this aim, first we expand the state and control variables based on the fractional order of Bernstein functions. Then we reduce the constraints of 2D-FOCP to a system of algebraic equations through the operational matrix. Now, one can solve straightforward the problem and drive the approximate solution of state and control variables. The convergence of the method in approximating the 2D-FOCP is proved. We demonstrate the efficiency and superiority of the method by comparing the results obtained by the presented method with the results of previous methods in some examples.

Resiliency and Stock Returns

2019 ◽  
Vol 33 (2) ◽  
pp. 747-782
Author(s):  
Jian Hua ◽  
Lin Peng ◽  
Robert A Schwartz ◽  
Nazli Sila Alan
Keyword(s):  
Stock Returns ◽  
Web Site ◽  
Trading Volume ◽  
Expected Returns ◽  
Control Variables ◽  
Oxford University ◽  
And Control ◽  
Published Paper ◽  
Extensive List ◽  
Oxford University Press

Abstract We present resiliency as a measure of liquidity and assess its relationship to expected returns. We establish a covariance-based measure, RES, that captures opening period resiliency, and use it to find a significant nonresiliency premium that ranges from 33 to 57 basis points per month. The premium persists after accounting for an extensive list of other liquidity-related measures and control variables. The results are significant for both value-weighted and equal-weighted returns, when micro-cap stocks are excluded, and for a sample of large cap stocks. The premium is particularly pronounced when trading volume is high. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Funding Liquidity Risk and the Dynamics of Hedge Fund Lockups

2020 ◽  
pp. 1-29
Author(s):  
Adam L. Aiken ◽  
Christopher P. Clifford ◽  
Jesse A. Ellis ◽  
Qiping Huang
Keyword(s):  
Hedge Funds ◽  
Regression Discontinuity ◽  
Hedge Fund ◽  
Liquidity Risk ◽  
Causal Link ◽  
Regression Discontinuity Design ◽  
Control Variables ◽  
Funding Liquidity ◽  
And Control ◽  
Funding Risk

Abstract We exploit the expiring nature of hedge fund lockups to create a new measure of funding liquidity risk that varies within funds. We find that hedge funds with lower funding risk generate higher returns, and this effect is driven by their increased exposure to equity-mispricing anomalies. Our results are robust to a variety of sampling criteria, variable definitions, and control variables. Further, we address endogeneity concerns in various ways, including a placebo approach and regression discontinuity design. Collectively, our results support a causal link between funding risk and the ability of managers to engage in risky arbitrage.

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