Successive Differences Research Articles

Spectro-temporal analysis of speech signals using zero-time windowing and group delay function

Traditional methods for estimating the vocal tract system characteristics typically compute the spectrum using a window size of 20–30ms. The resulting spectrum is the average characteristics of the vocal tract system within the window segment. Also, the effect of pitch harmonics need to be countered in the process of spectrum estimation. In this paper, we propose a new approach for estimating the spectrum using a highly decaying window function. The impulse-like window function used is an approximation to integration operation in the frequency domain, and the operation is referred to as zero-time windowing analogous to the zero-frequency filtering operation in frequency domain. The apparent loss in spectral resolution due to the use of a highly decaying window function is restored by successive differencing in the frequency domain. The spectral resolution is further improved by the use of group delay function which has an additive property on the individual resonances as against the multiplicative nature of the magnitude spectrum. The effectiveness of the proposed approach in estimating the spectrum is evaluated in terms of its robustness to additive noise, and in formant estimation.

Success Attributions within and across Organizations

Managers and organizations need to understand their surrounding environments to exhibit purposeful behaviour. Due to limited cognitive capacity their rationality is limited as well. Attribution and attributional research serves point of departure to capture how managers and organizations make sense of their internal and external environments, enabling them to act purposefully. Explores a set of tentative hypotheses in a small scale study and demonstrates differences in success attributions between managers and across managerial teams in high‐ and low‐performing organizations. Highlights theoretical and managerial implications.

A Successive Differences Method for Growth Curves with Missing Data and Random Observation Times

Abstract Incomplete growth curve data can be analyzed by the successive differences (SD) method, which uses the difference in consecutive pairs of observations for all subjects having two or more repeated measures. We have generalized it to handle varying observation times as well by partitioning the time interval spanned by all repeated measures into subintervals. The model is developed, including test statistics for the hypotheses of parallelism and of no change in response level over time, assuming parallelism. This generalized SD method is then applied to repeated serum cholesterol measurements on a subsample of 1,072 men from a prospective cohort study of 8,006 Hawaiian Japanese men living on Oahu.

A Successive Differences Method for Growth Curves with Missing Data and Random Observation Times

Abstract Incomplete growth curve data can be analyzed by the successive differences (SD) method, which uses the difference in consecutive pairs of observations for all subjects having two or more repeated measures. We have generalized it to handle varying observation times as well by partitioning the time interval spanned by all repeated measures into subintervals. The model is developed, including test statistics for the hypotheses of parallelism and of no change in response level over time, assuming parallelism. This generalized SD method is then applied to repeated serum cholesterol measurements on a subsample of 1,072 men from a prospective cohort study of 8,006 Hawaiian Japanese men living on Oahu.