Results 111 to 120 of about 47,794 (185)
Incomplete Gamma functions for evaluating Erlang process probabilities
, 1963 J. Whittlesey
semanticscholar +1 more source
Tests for Changes in Count Time Series Models With Exogenous Covariates
Journal of Time Series Analysis, EarlyView.ABSTRACT We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH‐X).
Šárka Hudecová, Marie Hušková
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On Exponential‐Family INGARCH Models
Journal of Time Series Analysis, EarlyView.ABSTRACT A range of integer‐valued generalised autoregressive conditional heteroscedastic (INGARCH) models have been proposed in the literature, including those based on conditional Poisson, negative binomial and Conway‐Maxwell‐Poisson distributions. This note considers a larger class of exponential‐family INGARCH models, showing that maximum empirical
Alan Huang +3 more
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Time‐Varying Dispersion Integer‐Valued GARCH Models
Journal of Time Series Analysis, EarlyView.ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza +3 more
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A Conditional Tail Expectation Type Risk Measure for Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the estimation of the conditional expectation 𝔼(Xh|X0>UX(1/p)), provided 𝔼|X0|<∞, at extreme levels, where (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$ is a strictly stationary time series, UX$$ {U}_X $$ its tail quantile function, h$$ h $$ is a positive integer and p∈(0,1)$$ p\in \left(0,1\right) $$ is such that p→0$$ p\to ...
Yuri Goegebeur +2 more
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Nonparametric Detection of a Time‐Varying Mean
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
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Testing for Unspecified Periodicities in Binary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT Given random variables Y1,…,Yn$$ {Y}_1,\dots, {Y}_n $$ with Yi∈{0,1}$$ {Y}_i\in \left\{0,1\right\} $$ we test the hypothesis whether the underlying success probabilities pi$$ {p}_i $$ are constant or whether they are periodic with an unspecified period length of r≥2$$ r\ge 2 $$.
Finn Schmidtke, Mathias Vetter
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A New Approach to Statistical Inference for Functional Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT The analysis of time‐indexed functional data plays an important role in the field of business and economic statistics. In the literature, statistical inference for functional time series often involves reducing the dimension of functional data to a finite dimension K$$ K $$, followed by the use of tools from multivariate analysis.
Hanjia Gao, Yi Zhang, Xiaofeng Shao
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Journal of Time Series Analysis, EarlyView.ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
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Journal of Time Series Analysis, EarlyView.ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka +4 more
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