Arbitrage-Free Call Option Surface Construction Using Regression Splines

Since Binnig, Rohrer and associates observed real-space topographic images of Si(111)-7×7 and invented the scanning tunneling microscope (STM),1) the STM has been accepted as a powerful surface science instrument.Recently, many application areas for the STM have been opened up, such as atomic force microscopy (AFM), magnetic force microscopy (MFM) and others. So, the STM technology holds a great promise for the future.The great advantages of the STM are its high spatial resolution in the lateral and vertical directions on the atomic scale. However, the STM has difficulty in identifying atomic images in a desired area because it uses piezoelectric (PZT) elements as a scanner.On the other hand, the demand to observe specimens under UHV condition has grown, along with the advent of the STM technology. The requirment of UHV-STM is especially very high in to study of surface construction of semiconductors and superconducting materials on the atomic scale. In order to improve the STM image quality by keeping the specimen and tip surfaces clean, we have built a new UHV-STM (JSTM-4000XV) system which is provided with other surface analysis capability.

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THE CALL OPTION IMPLICIT IN PROXY CONTESTED FIRMS

Review of Financial Economics ◽

10.1002/j.1873-5924.1992.tb00549.x ◽

1992 ◽

Vol 1 (2) ◽

pp. 53-67 ◽

Cited By ~ 1

Author(s):

G.D. Hancock

Keyword(s):

Call Option

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Models for Call Option Decisions

Financial Analysts Journal ◽

10.2469/faj.v24.n5.149 ◽

1968 ◽

Vol 24 (5) ◽

pp. 149-151 ◽

Cited By ~ 1

Author(s):

Jerome Bracken

Keyword(s):

Call Option

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New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research

10.1093/oso/9780190676001.003.0021 ◽

2018 ◽

Author(s):

Anne Buu ◽

Runze Li

Keyword(s):

Risk Factors ◽

Substance Abuse ◽

Longitudinal Data ◽

Developmental Trajectories ◽

Regression Splines ◽

Simulation Studies ◽

Process Data ◽

Measurement Reactivity ◽

Longitudinal Association ◽

Highly Correlated

This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.

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A method of equi-valued surface construction from volume data on a face-centered cubic lattice

Electronics and Communications in Japan (Part III Fundamental Electronic Science) ◽

10.1002/ecjc.10095 ◽

2003 ◽

Vol 86 (12) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Tomokazu Takahashi ◽

Tatsuhiro Yonekura ◽

Yoshiki Kishi

Keyword(s):

Face Centered Cubic ◽

Volume Data ◽

Cubic Lattice ◽

Face Centered ◽

Surface Construction

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Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Innovative Infrastructure Solutions ◽

10.1007/s41062-021-00568-z ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Chijioke Christopher Ikeagwuani

Keyword(s):

Random Forest ◽

Expansive Soil ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Regression Splines ◽

Gradient Boosting Machine ◽

Adaptive Regression ◽

Adaptive Regression Splines

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Multilayer surface construction for enhancing barrier properties of cellulose-based packaging

Carbohydrate Polymers ◽

10.1016/j.carbpol.2020.117431 ◽

2021 ◽

Vol 255 ◽

pp. 117431

Author(s):

Wangxia Wang ◽

Feng Gu ◽

Zhifei Deng ◽

Yang Zhu ◽

Jing Zhu ◽

...

Keyword(s):

Barrier Properties ◽

Surface Construction

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Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

Bilingualism Language and Cognition ◽

10.1017/s1366728921000079 ◽

2021 ◽

pp. 1-8

Author(s):

Koji Miwa ◽

Harald Baayen

Keyword(s):

Lexical Decision ◽

Tensor Product ◽

Nonlinear Interaction ◽

Mixed Model ◽

Nonlinear Effects ◽

Regression Splines ◽

Generalized Additive Mixed Model ◽

Generalized Additive Modeling ◽

Additive Modeling ◽

Cross Language

Abstract This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

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