Conditional co-occurrence probability acts like frequency in predicting fixation durations

The predictability of an upcoming word has been found to be a useful predictor in eye movement research, but is expensive to collect and subjective in nature. It would be desirable to have other predictors that are easier to collect and objective in nature if these predictors were capable of capturing the information stored in predictability. This paper contributes to this discussion by testing a possible predictor: conditional co-occurrence probability. This measure is a simple statistical representation of the relatedness of the current word to its context, based only on word co-occurrence patterns in data taken from the Internet. In the regression analyses, conditional co-occurrence probability acts like lexical frequency in predicting fixation durations, and its addition does not greatly improve the model fits. We conclude that readers do not seem to use the information contained within conditional co-occurrence probability during reading for meaning, and that similar simple measures of semantic relatedness are unlikely to be able to replace predictability as a predictor for fixation durations. Keywords: Co-occurrence probability, Cloze predictability, frequency, eye movement, fixation duration

Modeling the trading process on financial markets using the MSACD model

We propose a new framework for modeling time dependence in duration processes. The ACD approach introduced by Engle and Russell (1998) will be extended so that the conditional expectation of the durations depends on an unobservable stochastic process which is modeled via a Markov chain. The Markov switching ACD model (MSACD) is a flexible tool for description of financial duration processes. The introduction of a latent information regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C41, C22, C25, C51, G1

A statistical method for estimating activity uncertainty parameters to improve project forecasting

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy)

A Timing Model for Fast French

Models of speech timing are of both fundamental and applied interest. At the fundamental level, the prediction of time periods occupied by syllables and segments is required for general models of speech prosody and segmental structure. At the applied level, complete models of timing are an essential component of any speech synthesis system. Previous research has established that a large number of factors influence various levels of speech timing. Statistical analysis and modelling can identify order of importance and mutual influences between such factors. In the present study, a three-tiered model was created by a modified step-wise statistical procedure. It predicts the temporal structure of French, as produced by a single, highly fluent speaker at a fast speech rate (100 phonologically balanced sentences, hand-scored in the acoustic signal). The first tier models segmental influences due to phoneme type and contextual interactions between phoneme types. The second tier models syllable-level influences of lexical vs. grammatical status of the containing word, presence of schwa and the position within the word. The third tier models utterance-final lengthening. The complete segmental-syllabic model correlated with the original corpus of 1204 syllables at an overall r = 0.846. Residuals were normally distributed. An examination of subsets of the data set revealed some variation in the closeness of fit of the model. The results are considered to be useful for an initial timing model, particularly in a speech synthesis context. However, further research is required to extend the model to other speech rates and to examine inter-speaker variability in greater detail

Comparison of MSACD models

We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition, the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C22, C25, C41, G1

Nonparametric Kernel Testing in Semiparametric Autoregressive Conditional Duration Model

A crucially important advantage of the semiparametric regression approach to the nonlinear autoregressive conditional duration (ACD) model developed in Wongsaart et al. (2011), i.e. the so-called Semiparametric ACD (SEMI-ACD) model, is the fact that its estimation method does not require a parametric assumption on the conditional distribution of the standardized duration process and, therefore, the shape of the baseline hazard function. The research in this paper complements that of Wongsaart et al. (2011) by introducing a nonparametric procedure to test the parametric density function of ACD error through the use of the SEMI-ACD based residual. The hypothetical structure of the test is useful, not only to the establishment of a better parametric ACD model, but also to the specification testing of a number of financial market microstructure hypotheses, especially those related to the information asymmetry in finance. The testing procedure introduced in this paper differs in many ways from those discussed in existing literatures, for example Aït-Sahalia (1996), Gao and King (2004) and Fernandes and Grammig (2005). We show theoretically and experimentally the statistical validity of our testing procedure, while demonstrating its usefulness and practicality using datasets from New York and Australia Stock Exchange.Duration model, hazard rates and random measures, nonparametric kernel testing.

Revisiting the Status of Speech Rhythm

Text-to-Speech synthesis offers an interesting manner of synthesising various knowledge components related to speech production. To a certain extent, it provides a new way of testing the coherence of our understanding of speech production in a highly systematic manner. For example, speech rhythm and temporal organisation of speech have to be well-captured in order to mimic a speaker correctly. The simulation approach used in our laboratory for two languages supports our original hypothesis of multidimensionality and non-linearity in the production of speech rhythm. This paper presents an overview of our approach towards this issue, as it has been developed over the last years. We conceive the production of speech rhythm as a multidimensional task, and the temporal organisation of speech as a key component of this task (i.e., the establishment of temporal boundaries and durations). As a result of this multidimensionality, text-to-speech systems have to accommodate a number of systematic transformations and computations at various levels. Our model of the temporal organisation of read speech in French and German emerges from a combination of quantitative and qualitative parameters, organised according to psycholinguistic and linguistic structures. (An ideal speech synthesiser would also take into account subphonemic as well as pragmatic parameters. However such systems are not yet available)