Maximum Overdrive

Mel cepstral feature extraction technique is required in some or the other form in most of the latest speech and speaker recognition system. Here, first samples of speech are splitted into overlapping frames. Generally the length of frame is 25 ms and frame rate is 10 ms. Each and every frames are refined by pre-emphasis filter which amplifies higher frequencies. Next is to apply windowing so that Fourier spectrum for each windowing frame is achieved here Hamming window is used. To obtain vector

Jacqueline Adams Dickey 03/11/14 EDUC 661 Literature Review The increasing focus by many policymakers and measurement experts on using statistical models to evaluate teacher effectiveness has led to heightened debate regarding the usefulness of these methods but little discussion of the possible sources of error inherent in value-added modeling. Even the most robust models for making high-stakes decisions about teacher effectiveness contain numerous sources of error that can lead to ill-founded interpretations

specific variance. The shared variance contributes to the determination of factors [2]. There are a number of extraction methods available. In this paper the Maximum Likelihood Estimation method is used. Maximum Likelihood attempts to analyze the maximum likelihood of sampling the observed correlation matrix [9]. When applied to a data set, maximum-likelihood estimation provides

shown in this figure for all the models. It is clear from the figure that all the models have median waiting time closely, though, the Weibull model estimates little more median waiting time as compared to rest of the Gamma and Lognormal models. The maximum difference in the median waiting times are 1.77, 2.67 and 0.90 years between the Wibull and Gamma, Wibull and Lognormal, and Gamma and Lognormal models, respectively. All the three models have almost same median waiting times of

CCR and BCC models are used to measure the efficiency in a single stage. In order to explore the series relationship between the two-stage of production process or value chain. (ZhuoFan Yang, 2014) 1.2 Kao and Hwang (KH) Model Kao and Hwang (KH) Model proposed a two-stage DEA model where it is necessary for the weights associated with zdj to be the same. Their model for measuring the overall efficiency of DMUk. Kao and Hwang Model developed a different approach where the overall efficiency of the

To examine the effect of monetary policy on determination of coal prices in the U.S., we rely on a dynamic framework of cointegrated vector autoregression (CVAR). The prerequisite requirement for the application of the CVAR approach is that the selected variables must be nonstationary (i.e., I(1) series). The presence of a unit root in the variables is thus tested using the Dickey Fuller generalized least squares (DF-GLS) test (Elliott et al., 1996). Panel A of Table 1 reports the results of the

As shown in the above figure 1, the input neonatal image from the database is preprocessed using techniques such as noise removal, inhomogeneity and partial volume correction. Then from the preprocessed image the texture feature is extracted through EWT (Empirical Wavelet transform) and the most relevant features like Entropy, Correlation, Contrast, Homogeneity and Dissimilarity are chosen by GLCM (Gray Level Co-Occurrence Matrix). Based on the selected features the image is segmented by means of

1. Introduction The aim of this study is to investigate the stock price reaction of FirstEnergy and Allegheny to various merger related announcements. The study will also identify the presence of any run-up abnormal returns prior to the first formal announcement due to potential information leakages, insider trading or rumours. FirstEnergy is an energy company that formed in 1997, as a result of the merger between Ohio Edison and Centerior, which serves a large customer base. FirstEnergy and its

with missing data, LISREL by default uses the full information maximum likelihood (FIML) approach. However, users may also opt to impute missing values using either expectation maximization or Markov Chain Monte Carlo algorithms. Beginning with version 9.10, LISREL will automatically provide robust estimation of standard errors and chi-square statics if a raw file is used as a data input. The default estimation method in LISREL is Maximum Likelihood (ML) even if an estimated asymptotic covariance matrix

CHAPTER FOUR: RESEARCH METHODOLOGY In Chapter 4 will be described the methodology which was used. In this chapter, we will explain the reasons for choosing this methodology and give more details about this study. We will explain and present the methods that help us in this project. An overview of the method that was used to collect the data will be given. Afterwards, the statistical concepts will be explained thoroughly. 4.1 Data Collection This was a multicentre, prospective longitudinal