Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.]]>

Previously, Chang et al (2014) calculated the sample size and power for an overall BE test based on one superiority test (TEST vs. PLB) and an equivalence test (TEST vs. REF) using the joint distribution of sample means and sample variances because ’it is not easy to derive the sample size based on the multivariate t-distribution’ (we call this a ZChiSquare method). In this paper, we propose an exact method to calculate the power and sample size for an overall BE test based on two superiority tests (TEST vs. PLB, REF vs. PLB) and one equivalence test (TEST vs. REF) using a multivariate non-central t distribution directly, which we call an Exact-t method. We also extended the Z-ChiSquare method to an overall BE test with two superiority tests and one equivalence test, rather than one superiority and one equivalence test as in Chang et al’s paper.

Simulation shows that our proposed Exact-t method is computationally more efficient than the Z-ChiSquare method without self-writing codes to numerically calculate the conditional expectation of a multivariate normal distribution conditional upon a truncated Chi-Square distribution. When sample size is small, the Exact-t method generates more accurate results than the Z-ChiSquare method.

The Exact-t method is recommended when calculating power and determining sample size for a three-arm clinical endpoint BE study.]]>

Many authors, regardless of whether International Journal of Statistics and Probability publishes their work, appreciate the helpful feedback provided by the reviewers.

**Reviewers for Volume 8, Number 1**

Abdullah A. Smadi, Yarmouk University, Jordan

Afsin Sahin, Gazi University, Turkey

Ali Reza Fotouhi, University of the Fraser Valley, Canada

Anna Grana, University of Palermo, Italy

Carla J. Thompson, University of West Florida, USA

Felix Almendra-Arao, UPIITA del Instituto Politécnico Nacional , México

Gabriel A. Okyere, Kwame Nkrumah University of Science and Technology, Ghana

Gerardo Febres, Universidad Simón Bolívar, Venezuela

Hui Zhang, St. Jude Children’s Research Hospital, USA

Ivair R. Silva, Federal University of Ouro Preto – UFOP, Brazil

Krishna K. Saha, Central Connecticut State University, USA

Man Fung LO, Hong Kong Polytechnic University, Hong Kong

Olusegun Michael Otunuga, Marshall University, USA

Philip Westgate, University of Kentucky, USA

Qingyang Zhang, University of Arkansas, USA

Sajid Ali, Quaid-i-Azam University, Pakistan

Samir Khaled Safi, The Islamic University of Gaza, Palestine

Shatrunjai Pratap Singh, John Hancock Financial Services, USA

Sohair F. Higazi, University of Tanta, Egypt

Subhradev Sen, Alliance University, India

Vilda Purutcuoglu, Middle East Technical University (METU), Turkey

Vyacheslav Abramov, Swinburne University of Technology, Australia

Wei Zhang, The George Washington University, USA

Weizhong Tian, Eastern New Mexico University, USA

Zaixing Li, China University of Mining and Technology (Beijing), China

Wendy Smith

On behalf of,

The Editorial Board of International Journal of Statistics and Probability

Canadian Center of Science and Education

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