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FSTNFrame Span Traversal Nodes
FSTNFilm Super Twisted Nematic
FSTNFoil Compensated Super Twisted Nematic
FSTNFilm-compensated Super-Twisted Nematic
FSTNFormulated Super Twisted Nematic
FSTNFilm Super Twist Neumatic (barcode computer)
FSTNFast Super-Twisted Nematic
FSTNFederal Secure Telephone Network
FSTNFederal & State Tax Negotiators Incorporated (Renton, WA)
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References in periodicals archive ?
In Section 2, we briefly discuss the FSS distribution and FSTN distributions.
The above extension of (1) is referred to as flexible skew-t-normal (FSTN) distribution, denoted by FSTN([mu], [omega], [alpha], v).
It is noted that the FSTN distribution is proposed within the general framework of FSS distribution by combining with the definition of STN distribution and, as a consequence, it shares analogous feature with these two distributions.
Figure 1 displays the density functions of FSN and STN as well as FSTN distributions with four different situations considered, namely, [[alpha].sub.1] = 1, [[alpha].sub.3] = 0, and v = 10; [[alpha].sub.1] = [[alpha].sub.3] = 1 and v = 6; [[alpha].sub.1] = 1, [[alpha].sub.3] = -1, and v = 4; [[alpha].sub.1] = -1, [[alpha].sub.3] = 1, and v = 4, respectively, with [mu] = 1, [omega] = 1.5 for all cases.
To improve the efficiency of the algorithm and to facilitate statistical inference of the nonlinear models with FSTN distribution, we put forward the following profile likelihood method based on (3) and (6).
with two kinds of skewed distributions for random error as follows: Case (I): [epsilon] ~ STN([mu], [omega], [alpha], v) and Case (II): [epsilon] ~ FSTN ([mu], [lambda], [alpha], v).
Similar to previous analysis, each simulated data set is fitted under STN and FSN as well as FSTN scenarios using three different estimation algorithms.
To study the consistence properties of ML estimate, we focus on the situation that the true distribution for random error is Case (II) whereas the fitting distribution is FSTN too.
Tables 1 and 2 show that in general FSTN distribution enjoys more robustness and flexibility in modeling data with skewness and heavy tails as well as multimodality in comparison with other skewed alternatives and the implementation of MPNR method brings more accuracy and improvement for model estimation in the context of nonlinear regression with this new distribution.
Hantronix white LED backlight character displays have the following features: Black/white FSTN, Blue pixel/white background or vise versa in gray STN; easier to use compared to EL backlight: no inverter, no noise; lower power consumption; brighter than other LED backlight; and available at a competitive price.
* Developed high contract TN, STN & FSTN provides enhanced dot matrix module LCDs