Topics In Time Series Analysis And Forecasting

This thesis contains new developments in various topics in time series analysis and forecasting. These topics include: model selec- tion, estimation, forecasting and diagnostic checking.;In the area of model selection, finite and large sample properties of the commonly used selection criteria, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), are discussed. In the finite case, the study is limited to the two sample problem. The exact probability of selection is obtained for finite samples. The risk of each criterion is evaluated in the two sample situation. Empirical evidence regarding these risks are given for autoregressive processes. The asymptotic distribution of the (\u27)h is given, where (\u27)h is the estimate of the number of extra parameters in the model selected by the AIC criterion. This derivation is based on large sample properties of the likelihood ratio test statistic. The asymptotic distribution of the AIC in PAR models is also discussed.;In estimation, an explicit expression for the efficiency of strongly consistent estimates for the ARMA(1,1) model is derived. Empirical efficiency and the empirical estimate are examined by simulation.;On the topic of forecasting, the asymptotic variance of the fore- cast error is derived for an autoregressive model of first order. In the derivation, the estimated parameter is not assumed to be independ- ent of the data. The variance of the one-step forecast error is also derived for the fractional noise model.;In the last topic, empirical results for portmanteau test statistics are studied. It is shown that the modified Portmanteau test of Ljung and Box (1980) outperforms the modified test of Li and McLeod (1981). In testing for whiteness, the modified Portmanteau test is shown to have lower power than the cumulative periodogram test against both fractional noise and standard ARMA alternatives

Biomarker Associations with Efficacy of Abiraterone Acetate and Exemestane in Postmenopausal Patients with Estrogen Receptor–Positive Metastatic Breast Cancer

Purpose: Abiraterone may suppress androgens that stimulate breast cancer growth. We conducted a biomarker analysis of circulating tumor cells (CTCs), formalin-fixed paraffin-embedded tissues (FFPETs), and serum samples from postmenopausal estrogen receptor (ER)þ breast cancer patients to identify subgroups with differential abiraterone sensitivity. Methods: Patients (randomized 1:1:1) were treated with 1,000 mg/d abiraterone acetate þ 5 mg/d prednisone (AA), AA þ 25 mg/d exemestane (AAE), or exemestane. The biomarker population included treated patients (n = 293). The CTC population included patients with 3 baseline CTCs (n = 104). Biomarker [e.g., androgen receptor (AR), ER, Ki-67, CYP17] expression was evaluated. Cox regression stratified by prior therapies in the metastatic setting (0/1 vs. 2) and setting of letrozole/anastrozole (adjuvant vs. metastatic) was used to assess biomarker associations with progression-free survival (PFS). Results: Serum testosterone and estrogenlevels werelowered and progesterone increased with AA. Baseline AR or ER expression was not associated with PFS in CTCs or FFPETs for AAE versus exemestane, but dual positivity of AR and ER expression was associated with improved PFS [HR, 0.41; 95% confidence interval (CI), 0.16–1.07; P = 0.070]. For AR expression in FFPETs obtained <1 year prior to first dose (n = 67), a trend for improved PFS was noted for AAE versus exemestane (HR, 0.56; 95% CI, 0.24–1.33; P = 0.19). Conclusions: An AA pharmacodynamic effect was shown by decreased serum androgen and estrogen levels and increased progesterone. AR and ER dual expression in CTCs and newly obtained FFPETs may predict AA sensitivity

Abiraterone in Metastatic Prostate Cancer without Previous Chemotherapy

BackgroundAbiraterone acetate, an androgen biosynthesis inhibitor, improves overall survival in patients with metastatic castration-resistant prostate cancer after chemotherapy. We evaluated this agent in patients who had not received previous chemotherapy.MethodsIn this double-blind study, we randomly assigned 1088 patients to receive abiraterone acetate (1000 mg) plus prednisone (5 mg twice daily) or placebo plus prednisone. The coprimary end points were radiographic progression-free survival and overall survival.ResultsThe study was unblinded after a planned interim analysis that was performed after 43% of the expected deaths had occurred. The median radiographic progression-free survival was 16.5 months with abiraterone-prednisone and 8.3 months with prednisone alone (hazard ratio for abiraterone-prednisone vs. prednisone alone, 0.53; 95% confidence interval [CI], 0.45 to 0.62; P&lt;0.001). Over a median follow-up period of 22.2 months, overall survival was improved with abiraterone-prednisone (median not reached, vs. 27.2 months for prednisone alone; hazard ratio, 0.75; 95% CI, 0.61 to 0.93; P=0.01) but did not cross the efficacy boundary. Abiraterone-prednisone showed superiority over prednisone alone with respect to time to initiation of cytotoxic chemotherapy, opiate use for cancer-related pain, prostate-specific antigen progression, and decline in performance status. Grade 3 or 4 mineralocorticoid-related adverse events and abnormalities on liver-function testing were more common with abiraterone-prednisone.ConclusionsAbiraterone improved radiographic progression-free survival, showed a trend toward improved overall survival, and significantly delayed clinical decline and initiation of chemotherapy in patients with metastatic castration-resistant prostate cancer. (Funded by Janssen Research and Development, formerly Cougar Biotechnology; ClinicalTrials.gov number, NCT00887198.)