Coherent Predictions of Low Count Time Series

The application of traditional forecasting methods to discrete count data yields forecasts that are non-coherent. That is, such methods produce non-integer point and interval predictions which violate the restrictions on the sample space of the integer variable. This paper presents a methodology for producing coherent forecasts of low count time series. The forecasts are based on estimates of the p-step ahead predictive mass functions for a family of distributions nested in the integer-valued first-order autoregressive (INAR(1)) class. The predictive mass functions are constructed from convolutions of the unobserved components of the model, with uncertainty associated with both parameter values and model specifcation fully incorporated. The methodology is used to analyse two sets of Canadian wage loss claims data.Forecasting; Discrete Time Series; INAR(1); Bayesian Prediction; Bayesian Model Averaging.

Testing for Dependence in Non-Gaussian Time Series Data

This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete, observations. Locally most powerful tests for various forms of dependence are derived, based on an approximate likelihood function. Invariance to the distribution adopted for the data, conditional on the latent process, is shown to hold in certain cases. The tests are applied to various financial data sets, and Monte Carlo experiments used to gauge their finite sample properties.Latent variable model; locally most powerful tests; approximate likelihood; correlation tests; stochastic volatility tests.

Approximate Bayesian Computation in State Space Models

A new approach to inference in state space models is proposed, based on approximate Bayesian computation (ABC). ABC avoids evaluation of the likelihood function by matching observed summary statistics with statistics computed from data simulated from the true process; exact inference being feasible only if the statistics are sufficient. With finite sample sufficiency unattainable in the state space setting, we seek asymptotic sufficiency via the maximum likelihood estimator (MLE) of the parameters of an auxiliary model. We prove that this auxiliary model-based approach achieves Bayesian consistency, and that - in a precise limiting sense - the proximity to (asymptotic) sufficiency yielded by the MLE is replicated by the score. In multiple parameter settings a separate treatment of scalar parameters, based on integrated likelihood techniques, is advocated as a way of avoiding the curse of dimensionality. Some attention is given to a structure in which the state variable is driven by a continuous time process, with exact inference typically infeasible in this case as a result of intractable transitions. The ABC method is demonstrated using the unscented Kalman filter as a fast and simple way of producing an approximation in this setting, with a stochastic volatility model for financial returns used for illustration

Testing for Dependence in Non-Gaussian Time Series Data

This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete, observations. Locally most powerful tests for various forms of dependence are derived, based on an approximate likelihood function. Invariance to the distribution adopted for the data, conditional on the latent process, is shown to hold in certain cases. The tests are applied to various financial data sets, and Monte Carlo experiments used to gauge their finite sample propertiesLatent variable model; locally most powerful tests; approximate likelihood; correlation tests; stochastic volatility tests

Optimal Probabilistic Forecasts for Counts

Optimal probabilistic forecasts of integer-valued random variables are derived. The optimality is achieved by estimating the forecast distribution nonparametrically over a given broad model class and proving asymptotic efficiency in that setting. The ideas are demonstrated within the context of the integer autoregressive class of models, which is a suitable class for any count data that can be interpreted as a queue, stock, birth and death process or branching process. The theoretical proofs of asymptotic optimality are supplemented by simulation results which demonstrate the overall superiority of the nonparametric method relative to a misspecified parametric maximum likelihood estimator, in large but .nite samples. The method is applied to counts of wage claim benefits, stock market iceberg orders and civilian deaths in Iraq, with bootstrap methods used to quantify sampling variation in the estimated forecast distributions.Nonparametric Inference; Asymptotic Efficiency; Count Time Series; INAR Model Class; Bootstrap Distributions; Iceberg Stock Market Orders.

Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models

The object of this paper is to produce non-parametric maximum likelihood estimates of forecast distributions in a general non-Gaussian, non-linear state space setting. The transition densities that define the evolution of the dynamic state process are represented in parametric form, but the conditional distribution of the non-Gaussian variable is estimated non-parametrically. The filtering and prediction distributions are estimated via a computationally efficient algorithm that exploits the functional relationship between the observed variable, the state variable and a measurement error with an invariant distribution. Simulation experiments are used to document the accuracy of the non-parametric method relative to both correctly and incorrectly specified parametric alternatives. In an empirical illustration, the method is used to produce sequential estimates of the forecast distribution of realized volatility on the S&P500 stock index during the recent financial crisis. A resampling technique for measuring sampling variation in the estimated forecast distributions is also demonstrated.Probabilistic Forecasting; Non-Gaussian Time Series; Grid-based Filtering; Penalized Likelihood; Subsampling; Realized Volatility.

Interventions for treating oral mucositis for patients with cancer receiving treatment

Background Treatment of cancer is increasingly effective but is associated with short and long term side effects. Oral and gastrointestinal side effects, including oral candidiasis, remain a major source of illness despite the use of a variety of agents to treat them. Objectives To assess the effectiveness of interventions for the treatment of oral candidiasis for patients with cancer receiving chemotherapy or radiotherapy or both. Search strategy Computerised searches of Cochrane Oral Health Group and PaPaS Trials Registers (to 1 June 2010), CENTRAL via the Cochrane Library (Issue 2, 2010, 1 June 2010), MEDLINE via OVID (1 June 2010), EMBASE via OVID (1 June 2010), CINAHL via EBSCO (1 June 2010), CANCERLIT via PubMed (1 June 2010), OpenSIGLE (1 June 2010) and LILACS via Virtual Health Library (1 June 2010) were undertaken. Reference lists fromrelevant articles were searched and the authors of eligible trials were contacted to identify trials and obtain additional information. Selection criteria All randomised controlled trials comparing agents prescribed to treat oral candidiasis in people receiving chemotherapy or radiotherapy for cancer. The outcomes were eradication of oral candidiasis, dysphagia, systemic infection, amount of analgesia, length of hospitalisation, cost and patient quality of life. Data collection and analysis Data were independently extracted, in duplicate, by two review authors. Trial authors were contacted for details of randomisation and withdrawals and a quality assessment was carried out. Risk ratios (RR) were calculated using fixed-effect models. Main results Ten trials involving 940 patients, satisfied the inclusion criteria and are included in this review. Drugs absorbed from the gastrointestinal (GI) tract were beneficial in eradication of oral candidiasis compared with drugs not absorbed from the GI tract (three trials: RR = 1.29, 95% confidence interval (CI) 1.09 to 1.52), however there was significant heterogeneity. A drug absorbed from the GI tract, ketoconazole, wasmore beneficial than placebo in eradicating oral candidiasis (one trial: RR = 3.61, 95% CI 1.47 to 8.88). Clotrimazole, at a higher dose of 50 mg was more effective than a lower 10 mg dose in eradicating oral candidiasis, when assessed mycologically (one trial: RR = 2.00, 95% CI 1.11 to 3.60). Only one of the ten trials was assessed as at low risk of bias. Authors' conclusions There is insufficient evidence to claimor refute a benefit for any antifungal agent in treating candidiasis. Further well designed, placebo-controlled trials assessing the effectiveness of old and new interventions for treating oral candidiasis are needed. Clinicians need to make a decision on whether to prevent or treat oral candidiasis in patients receiving treatment for cancer. This review is published as a Cochrane Review in the Cochrane Database of Systematic Reviews 2010, Issue 7. Cochrane Reviews are regularly updated as new evidence emerges and in response to comments and criticisms, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Review.</p

Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models

A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior. With no reduction to a low-dimensional set of sufficient statistics being possible in the state space setting, we define the summaries as the maximum of an auxiliary likelihood function, and thereby exploit the asymptotic sufficiency of this estimator for the auxiliary parameter vector. We derive conditions under which this approach - including a computationally efficient version based on the auxiliary score - achieves Bayesian consistency. To reduce the well-documented inaccuracy of ABC in multi-parameter settings, we propose the separate treatment of each parameter dimension using an integrated likelihood technique. Three stochastic volatility models for which exact Bayesian inference is either computationally challenging, or infeasible, are used for illustration. We demonstrate that our approach compares favorably against an extensive set of approximate and exact comparators. An empirical illustration completes the paper.Comment: This paper is forthcoming at the Journal of Computational and Graphical Statistics. It also supersedes the earlier arXiv paper "Approximate Bayesian Computation in State Space Models" (arXiv:1409.8363

Interventions for preventing oral mucositis for patients with cancer receiving treatment

Background: Treatment of cancer with chemotherapy is becoming increasingly more effective but is associated with short and long-term side effects. Oral side effects remain a major source of illness despite the use of a variety of agents to prevent them. Objectives: To evaluate the effectiveness of oral (and topical) prophylactic agents for oral mucositis and oral candidiasis in patients with cancer (excluding head and neck cancer), compared with placebo or no treatment. Search Strategy: Computerised MEDLINE, EMBASE, CINAHL, CANCERLIT, the Cochrane Controlled Trials Register and the Cochrane Oral Health Group Specialist Register search up to July 1999. Reference lists from relevant articles were scanned and the authors of eligible studies were contacted to identify trials and obtain additional information. Selection Criteria: Studies were selected if they met the following criteria: design - random or quasi-random allocation of participants; participants - anyone with cancer receiving chemotherapy (excluding head and neck cancer); interventions - prophylactic agents prescribed to reduce oral conditions arising from cancer or its treatment; outcomes - mucositis and oral candidiasis. Data Collection and Analysis: Information regarding methods, participants, interventions and outcome measures and results were independently extracted, in duplicate, by two reviewers (JC &amp; HW). Specialist advice was sought to categorise interventions. Authors were contacted for details of randomisation and withdrawals and a quality assessment was carried out using the Jadad criteria (Jadad 1998). The Cochrane Oral Health Group statistical guidelines were followed and relative risk values calculated using random effects models where significant heterogeneity was detected (P &lt; 0.1). Main Results: Thirty-eight reports of trials were initially included. Two were duplicate reports and nine were excluded as there was no useable information. Of the 27 useable studies 14 had data for mucositis comprising 945 randomised patients and 15 included data for oral candidiasis with 1164 randomised patients. Of the eight prophylactic agents used for mucositis only one, ice chips, was effective (Relative risk 0.57, 95% CI 0.43 to 0.77, chi-square for heterogeneity = 0.26 (df = 1), p = 0.61). The NNT to prevent one extra case of mucositis over the baseline incidence using ice chips was 4 (95%CI: 3 to 7). The NNT for when the baseline incidence of mucositis in the population ranges from 50% to 80% are 5 to 4 respectively. There is evidence that antifungal agents which are partially or fully absorbed from the gastrointestinal tract prevent oral candidiasis and that the partially absorbed agents may be more effective than the fully absorbed agents. The RR for partially absorbed agents was 0.13 (95% CI 0.06 to 0.27, chi-square for heterogeneity = 5.3 (df = 3), P = 0. 15). The NNT to prevent one extra case of oral candidiasis over the baseline incidence using partially absorbed drugs was 3 (95% CI: 3 to 5). The NNT for when the baseline incidence of oral candidiasis in the population ranges from 30% to 70% are 4 to 2 respectively. The general reporting of RCT's was poor however the median Jadad score was acceptable and improved further when the authors provided additional information. The sensitivity analysis confirmed the findings for oral candidiasis. Reviewer's Conclusions: There is some evidence that ice chips prevent mucositis. None of the other prophylactic agents included in this review prevented mucositis. There is evidence that prophylactic use of antifungal agents which are absorbed or partially absorbed from the gastrointestinal tract reduce the clinical signs of oral candidiasis, and the partially absorbed drugs may be more effective. Future trials in this area should address the link between oral and general health including outcomes relevant to the patient. Collaboration between medical and dental teams is indicated.</p

Effect of Hesperidin with and without a Calcium (Calcilock®) Supplement on Bone Health in Postmenopausal Women

Context: Citrus fruits contain unique flavanones. One of the most abundant of the flavanones, hesperidin, has been shown to prevent bone loss in ovariectomized rats. Objective: The objective of the study was to measure the effect of hesperidin with or without calcium supplementation on bone calcium retention in postmenopausal women. Design: The study was a double-blind, placebo-controlled, randomized-order crossover design of 500 g hesperidin with or without 500 mg calcium supplement in 12 healthy postmenopausal women. Bone calcium retention was determined from urinary excretion of the rare isotope, 41Ca, from bone. Results: Calcium plus hesperidin, but not hesperidin alone, improved bone calcium retention by 5.5% (P < .04). Conclusion: Calcium supplementation (Calcilock), in combination with hesperidin, is effective at preserving bone in postmenopausal women. - See more at: http://press.endocrine.org/doi/10.1210/jc.2015-3767#sthash.ztalWWcv.dpu