# Bootstrap Inference for PMM2 Models

#### Serhii Zabolotnii

#### 2026-05-29

## Introduction

Statistical inference—constructing confidence intervals, testing hypotheses, and quantifying uncertainty—is central to data analysis. For PMM2 estimators, bootstrap methods provide a powerful, distribution-free approach to inference that:

1. Does not assume normality of parameter estimates
2. Accounts for skewness in error distributions
3. Provides accurate confidence intervals even with moderate sample sizes
4. Enables hypothesis testing without restrictive assumptions

This vignette provides a comprehensive guide to bootstrap inference for both linear regression and time series models fitted with PMM2.

## Bootstrap Basics

### The Bootstrap Principle

The bootstrap, introduced by Efron (1979), resamples from the data to estimate the sampling distribution of a statistic. For regression models:

1. Fit the model to obtain parameter estimates \(\hat{\beta}\) and residuals \(\hat{e}_i\)
2. Resample residuals with replacement: \(e_i^* \sim \{\hat{e}_1, \ldots, \hat{e}_n\}\)
3. Generate bootstrap responses: \(y_i^* = \mathbf{x}_i^T \hat{\beta} + e_i^*\)
4. Refit the model to \((X, y^*)\) to get \(\hat{\beta}^*\)
5. Repeat steps 2-4 many times (typically 500-2000)
6. Estimate uncertainty from the distribution of \(\{\hat{\beta}^*_1, \ldots, \hat{\beta}^*_B\}\)

## Setup

    library(EstemPMM)
    set.seed(2025)

## Part 1: Bootstrap for Linear Regression

### Basic Example: Simple Linear Regression

    # Generate data with skewed errors
    n <- 200
    x <- rnorm(n, mean = 5, sd = 2)

    # True parameters
    beta_0 <- 10
    beta_1 <- 2.5

    # Skewed errors: chi-squared distribution
    errors <- rchisq(n, df = 4) - 4

    # Response
    y <- beta_0 + beta_1 * x + errors
    dat <- data.frame(y = y, x = x)

    # Fit PMM2 model
    fit_pmm2 <- lm_pmm2(y ~ x, data = dat, verbose = FALSE)

    # Perform bootstrap inference
    boot_results <- pmm2_inference(fit_pmm2,
                                   formula = y ~ x,
                                   data = dat,
                                   B = 1000,
                                   seed = 123)

    # Display summary
    summary(boot_results)
    #>     Estimate       Std.Error            bias               t.value
    #>  Min.   :2.563   Min.   :0.07524   Min.   :-0.0028871   Min.   :22.84
    #>  1st Qu.:4.352   1st Qu.:0.16282   1st Qu.:-0.0009472   1st Qu.:25.65
    #>  Median :6.142   Median :0.25041   Median : 0.0009928   Median :28.45
    #>  Mean   :6.142   Mean   :0.25041   Mean   : 0.0009928   Mean   :28.45
    #>  3rd Qu.:7.931   3rd Qu.:0.33799   3rd Qu.: 0.0029327   3rd Qu.:31.26
    #>  Max.   :9.721   Max.   :0.42558   Max.   : 0.0048726   Max.   :34.07
    #>     p.value              conf.low       conf.high
    #>  Min.   : 0.000e+00   Min.   :2.416   Min.   : 2.710
    #>  1st Qu.:4.468e-116   1st Qu.:4.033   1st Qu.: 4.672
    #>  Median :8.936e-116   Median :5.651   Median : 6.633
    #>  Mean   :8.936e-116   Mean   :5.651   Mean   : 6.633
    #>  3rd Qu.:1.340e-115   3rd Qu.:7.269   3rd Qu.: 8.594
    #>  Max.   :1.787e-115   Max.   :8.887   Max.   :10.555

Interpretation:

- Estimate: PMM2 parameter estimates from original sample
- Std.Error: Bootstrap standard error (SD of bootstrap distribution)
- CI.Lower / CI.Upper: 95% confidence intervals
- t.value: Test statistic for H₀: β = 0
- p.value: Two-sided p-value

### Understanding Bootstrap Distributions

    # Extract bootstrap samples for visualization
    # (Note: This requires accessing internals - in practice, use summary output)

    # Refit to get bootstrap samples
    set.seed(123)
    B <- 1000
    boot_samples <- matrix(0, nrow = B, ncol = 2)

    for(b in 1:B) {
      # Resample residuals
      res_b <- sample(residuals(fit_pmm2), size = n, replace = TRUE)

      # Generate bootstrap data
      y_b <- fitted(fit_pmm2) + res_b
      dat_b <- dat
      dat_b$y <- y_b

      # Refit
      fit_b <- lm_pmm2(y ~ x, data = dat_b, verbose = FALSE)
      boot_samples[b, ] <- coef(fit_b)
    }

    # Plot bootstrap distributions
    par(mfrow = c(2, 2))

    # Intercept
    hist(boot_samples[, 1], breaks = 30, main = "Bootstrap: Intercept",
         xlab = "Intercept", col = "lightblue", border = "white")
    abline(v = beta_0, col = "red", lwd = 2, lty = 2)
    abline(v = coef(fit_pmm2)[1], col = "darkblue", lwd = 2)
    legend("topright", legend = c("True", "Estimate"),
           col = c("red", "darkblue"), lty = c(2, 1), lwd = 2, cex = 0.8)

    # Q-Q plot for intercept
    qqnorm(boot_samples[, 1], main = "Q-Q Plot: Intercept")
    qqline(boot_samples[, 1], col = "red", lwd = 2)

    # Slope
    hist(boot_samples[, 2], breaks = 30, main = "Bootstrap: Slope",
         xlab = "Slope", col = "lightgreen", border = "white")
    abline(v = beta_1, col = "red", lwd = 2, lty = 2)
    abline(v = coef(fit_pmm2)[2], col = "darkblue", lwd = 2)
    legend("topright", legend = c("True", "Estimate"),
           col = c("red", "darkblue"), lty = c(2, 1), lwd = 2, cex = 0.8)

    # Q-Q plot for slope
    qqnorm(boot_samples[, 2], main = "Q-Q Plot: Slope")
    qqline(boot_samples[, 2], col = "red", lwd = 2)

    par(mfrow = c(1, 1))

Observations:

- Bootstrap distributions are approximately normal (central limit theorem)
- Q-Q plots show minor deviations from normality due to error skewness
- Bootstrap confidence intervals account for this non-normality

## Part 2: Confidence Interval Methods

The `pmm2_inference()` function provides multiple CI methods:

### Percentile Method

The simplest approach: use empirical quantiles of the bootstrap distribution.

\[ CI_{95\%} = \left[ \hat{\beta}^*_{(0.025)}, \hat{\beta}^*_{(0.975)} \right] \]

Advantages: Simple, transformation-respecting Disadvantages: Can be biased if bootstrap distribution is skewed

### Bias-Corrected (BC) Method

Adjusts for bias in the bootstrap distribution:

\[ \text{Bias} = \mathbb{E}[\hat{\beta}^*] - \hat{\beta} \]

Advantages: Corrects systematic bias Disadvantages: Requires larger B (≥ 1000)

### Comparison with OLS

    # Fit OLS for comparison
    fit_ols <- lm(y ~ x, data = dat)

    # Extract standard errors and CIs
    se_ols <- summary(fit_ols)$coefficients[, "Std. Error"]
    ci_ols <- confint(fit_ols)

    # Bootstrap CIs (from summary)
    boot_summary <- boot_results

    # Create comparison table
    comparison <- data.frame(
      Parameter = c("Intercept", "Slope"),
      True = c(beta_0, beta_1),
      PMM2_Estimate = coef(fit_pmm2),
      OLS_SE = se_ols,
      PMM2_Boot_SE = boot_summary$Std.Error,
      SE_Ratio = boot_summary$Std.Error / se_ols
    )

    print(comparison, row.names = FALSE, digits = 3)
    #>  Parameter True PMM2_Estimate OLS_SE PMM2_Boot_SE SE_Ratio
    #>  Intercept 10.0          9.72   0.59       0.4256    0.721
    #>      Slope  2.5          2.56   0.11       0.0752    0.686

    cat("\n=== Confidence Interval Comparison ===\n\n")
    #>
    #> === Confidence Interval Comparison ===
    cat("OLS 95% CI (Normal-based):\n")
    #> OLS 95% CI (Normal-based):
    print(ci_ols)
    #>                2.5 %    97.5 %
    #> (Intercept) 8.639023 10.967187
    #> x           2.330172  2.762934

    cat("\nPMM2 95% CI (Bootstrap Percentile):\n")
    #>
    #> PMM2 95% CI (Bootstrap Percentile):
    print(data.frame(
      Parameter = rownames(ci_ols),
      Lower = boot_summary$conf.low,
      Upper = boot_summary$conf.high
    ))
    #>     Parameter    Lower    Upper
    #> 1 (Intercept) 8.886616 10.55485
    #> 2           x 2.415535  2.71046

Key Finding: PMM2 bootstrap standard errors are typically 10-30% smaller than OLS standard errors when errors are skewed, reflecting higher statistical efficiency.

## Part 3: Hypothesis Testing

Bootstrap enables flexible hypothesis testing without normality assumptions.

### Testing Individual Coefficients

    # H0: Slope = 0 (no relationship)
    # From bootstrap summary
    boot_summary <- boot_results

    cat("=== Hypothesis Test: Slope = 0 ===\n\n")
    #> === Hypothesis Test: Slope = 0 ===
    cat("t-statistic:", boot_summary$t.value[2], "\n")
    #> t-statistic: 34.06547
    cat("p-value:    ", boot_summary$p.value[2], "\n\n")
    #> p-value:     2.395759e-254

    if(boot_summary$p.value[2] < 0.05) {
      cat("Decision: Reject H0 at 5% level\n")
      cat("Conclusion: Significant relationship between x and y\n")
    } else {
      cat("Decision: Fail to reject H0\n")
    }
    #> Decision: Reject H0 at 5% level
    #> Conclusion: Significant relationship between x and y

### Testing Equality of Coefficients

For multiple predictors, we might test if two slopes are equal.

    # Generate multiple regression data
    n <- 250
    x1 <- rnorm(n)
    x2 <- rnorm(n)
    errors <- rexp(n, rate = 1) - 1

    # True model: similar coefficients
    beta <- c(5, 1.5, 1.8, -0.5)
    y <- beta[1] + beta[2]*x1 + beta[3]*x2 + beta[4]*x1*x2 + errors

    dat_multi <- data.frame(y = y, x1 = x1, x2 = x2)

    # Fit model
    fit_multi <- lm_pmm2(y ~ x1 + x2 + x1:x2, data = dat_multi, verbose = FALSE)

    # Bootstrap
    boot_multi <- pmm2_inference(fit_multi,
                                 formula = y ~ x1 + x2 + x1:x2,
                                 data = dat_multi,
                                 B = 1000,
                                 seed = 456)

    # Test H0: beta_x1 = beta_x2
    summary(boot_multi)
    #>     Estimate         Std.Error            bias               t.value
    #>  Min.   :-0.5119   Min.   :0.04254   Min.   :-0.0053331   Min.   :-12.03
    #>  1st Qu.: 1.0492   1st Qu.:0.04258   1st Qu.:-0.0012402   1st Qu.: 24.63
    #>  Median : 1.6523   Median :0.04313   Median : 0.0006678   Median : 38.29
    #>  Mean   : 1.9362   Mean   :0.04838   Mean   :-0.0004654   Mean   : 35.26
    #>  3rd Qu.: 2.5392   3rd Qu.:0.04893   3rd Qu.: 0.0014426   3rd Qu.: 48.92
    #>  Max.   : 4.9521   Max.   :0.06474   Max.   : 0.0021360   Max.   : 76.49
    #>     p.value             conf.low         conf.high
    #>  Min.   :0.000e+00   Min.   :-0.5953   Min.   :-0.4286
    #>  1st Qu.:0.000e+00   1st Qu.: 0.9658   1st Qu.: 1.1327
    #>  Median :0.000e+00   Median : 1.5677   Median : 1.7368
    #>  Mean   :5.919e-34   Mean   : 1.8413   Mean   : 2.0310
    #>  3rd Qu.:5.919e-34   3rd Qu.: 2.4433   3rd Qu.: 2.6351
    #>  Max.   :2.368e-33   Max.   : 4.8252   Max.   : 5.0790

## Part 4: Bootstrap for Time Series Models

Time series bootstrap requires special care due to temporal dependence.

### AR Model Bootstrap

    # Generate AR(1) data with skewed innovations
    n <- 300
    phi <- 0.7
    innovations <- rgamma(n, shape = 2, rate = 2) - 1

    x <- numeric(n)
    x[1] <- innovations[1]
    for(i in 2:n) {
      x[i] <- phi * x[i-1] + innovations[i]
    }

    # Fit AR(1) model
    fit_ar <- ar_pmm2(x, order = 1, method = "pmm2", include.mean = FALSE)

    # Bootstrap inference for time series
    boot_ar <- ts_pmm2_inference(fit_ar,
                                 B = 1000,
                                 seed = 789)

    # Summary
    summary(boot_ar)
    #>     Estimate        Std.Error            bias               t.value
    #>  Min.   :0.6787   Min.   :0.03428   Min.   :-2.214e-05   Min.   :19.8
    #>  1st Qu.:0.6787   1st Qu.:0.03428   1st Qu.:-2.214e-05   1st Qu.:19.8
    #>  Median :0.6787   Median :0.03428   Median :-2.214e-05   Median :19.8
    #>  Mean   :0.6787   Mean   :0.03428   Mean   :-2.214e-05   Mean   :19.8
    #>  3rd Qu.:0.6787   3rd Qu.:0.03428   3rd Qu.:-2.214e-05   3rd Qu.:19.8
    #>  Max.   :0.6787   Max.   :0.03428   Max.   :-2.214e-05   Max.   :19.8
    #>     p.value             conf.low        conf.high
    #>  Min.   :3.163e-87   Min.   :0.6115   Min.   :0.7459
    #>  1st Qu.:3.163e-87   1st Qu.:0.6115   1st Qu.:0.7459
    #>  Median :3.163e-87   Median :0.6115   Median :0.7459
    #>  Mean   :3.163e-87   Mean   :0.6115   Mean   :0.7459
    #>  3rd Qu.:3.163e-87   3rd Qu.:0.6115   3rd Qu.:0.7459
    #>  Max.   :3.163e-87   Max.   :0.6115   Max.   :0.7459

    # Check if AR coefficient is significantly different from 0.5
    cat("\n=== Testing H0: phi = 0.5 ===\n")
    #>
    #> === Testing H0: phi = 0.5 ===
    boot_summary_ar <- boot_ar
    ci_lower <- boot_summary_ar$conf.low[1]
    ci_upper <- boot_summary_ar$conf.high[1]

    cat("95% CI: [", round(ci_lower, 3), ",", round(ci_upper, 3), "]\n")
    #> 95% CI: [ 0.612 , 0.746 ]

    if(0.5 >= ci_lower && 0.5 <= ci_upper) {
      cat("Decision: Cannot reject H0 (0.5 is within CI)\n")
    } else {
      cat("Decision: Reject H0 (0.5 is outside CI)\n")
    }
    #> Decision: Reject H0 (0.5 is outside CI)

### ARMA Model Bootstrap

    # Generate ARMA(1,1) with heavy-tailed innovations
    n <- 400
    phi <- 0.5
    theta <- 0.4
    innovations <- rt(n, df = 4)

    x <- arima.sim(n = n,
                   model = list(ar = phi, ma = theta),
                   innov = innovations)

    # Fit ARMA model
    fit_arma <- arma_pmm2(x, order = c(1, 1), method = "pmm2", include.mean = FALSE)

    # Bootstrap
    boot_arma <- ts_pmm2_inference(fit_arma,
                                   B = 1000,
                                   seed = 999,
                                   method = "block",
                                   block_length = 20)

    # Display results
    summary(boot_arma)
    #>     Estimate        Std.Error            bias             t.value
    #>  Min.   :0.3020   Min.   :0.07011   Min.   :-0.04003   Min.   :3.948
    #>  1st Qu.:0.3682   1st Qu.:0.07170   1st Qu.:-0.03791   1st Qu.:4.982
    #>  Median :0.4344   Median :0.07330   Median :-0.03579   Median :6.016
    #>  Mean   :0.4344   Mean   :0.07330   Mean   :-0.03579   Mean   :6.016
    #>  3rd Qu.:0.5006   3rd Qu.:0.07490   3rd Qu.:-0.03368   3rd Qu.:7.051
    #>  Max.   :0.5668   Max.   :0.07650   Max.   :-0.03156   Max.   :8.085
    #>     p.value             conf.low        conf.high
    #>  Min.   :0.000e+00   Min.   :0.1521   Min.   :0.4519
    #>  1st Qu.:1.970e-05   1st Qu.:0.2214   1st Qu.:0.5150
    #>  Median :3.941e-05   Median :0.2907   Median :0.5781
    #>  Mean   :3.941e-05   Mean   :0.2907   Mean   :0.5781
    #>  3rd Qu.:5.911e-05   3rd Qu.:0.3601   3rd Qu.:0.6411
    #>  Max.   :7.881e-05   Max.   :0.4294   Max.   :0.7042

Key Advantage: Bootstrap inference is robust to innovation distribution and does not require Gaussian assumptions.

## Part 5: Choosing Bootstrap Parameters

### Number of Bootstrap Samples (B)

The choice of B affects precision and computation time:

Purpose | Recommended B | Computation Time
--- | --- | ---
Quick check | 200-500 | Seconds
Standard inference | 1000-2000 | Minutes
Publication-quality | 5000-10000 | Hours

    # Assess stability of bootstrap SEs with different B
    B_values <- c(100, 500, 1000, 2000)
    se_results <- data.frame(B = B_values, SE_Intercept = NA, SE_Slope = NA)

    for(i in seq_along(B_values)) {
      boot_temp <- pmm2_inference(fit_pmm2, formula = y ~ x, data = dat,
                                  B = B_values[i], seed = 123)
      summary_temp <- boot_temp
      se_results$SE_Intercept[i] <- summary_temp$Std.Error[1]
      se_results$SE_Slope[i] <- summary_temp$Std.Error[2]
    }

    print(se_results)

Rule of Thumb: Use B ≥ 1000 for confidence intervals, B ≥ 2000 for hypothesis tests.

### Parallel Bootstrap

For large datasets or complex models, use parallel computing:

    # Enable parallel bootstrap (requires 'parallel' package)
    boot_parallel <- pmm2_inference(fit_pmm2,
                                    formula = y ~ x,
                                    data = dat,
                                    B = 2000,
                                    seed = 123,
                                    parallel = TRUE,
                                    cores = 4)  # Use 4 CPU cores

    # Check speedup
    # Typical speedup: 3-4x on 4 cores

Performance Tip: Parallel bootstrap is most beneficial when:

- B ≥ 1000
- Model fitting is computationally intensive (ARMA, ARIMA)
- Multiple cores available

## Part 6: Diagnostic Checks

### Convergence of Bootstrap Estimates

Check if bootstrap replicates converged successfully:

    # Rerun bootstrap with verbose output for diagnostics
    boot_verbose <- pmm2_inference(fit_pmm2,
                                   formula = y ~ x,
                                   data = dat,
                                   B = 500,
                                   seed = 321)

    # Check for failed bootstrap samples
    # (In production code, inspect convergence attribute)
    summary(boot_verbose)
    #>     Estimate       Std.Error            bias               t.value
    #>  Min.   :2.563   Min.   :0.07911   Min.   :-0.0114838   Min.   :21.12
    #>  1st Qu.:4.352   1st Qu.:0.17442   1st Qu.:-0.0083642   1st Qu.:23.94
    #>  Median :6.142   Median :0.26974   Median :-0.0052445   Median :26.76
    #>  Mean   :6.142   Mean   :0.26974   Mean   :-0.0052445   Mean   :26.76
    #>  3rd Qu.:7.931   3rd Qu.:0.36505   3rd Qu.:-0.0021249   3rd Qu.:29.58
    #>  Max.   :9.721   Max.   :0.46037   Max.   : 0.0009947   Max.   :32.40
    #>     p.value             conf.low       conf.high
    #>  Min.   :0.000e+00   Min.   :2.408   Min.   : 2.718
    #>  1st Qu.:1.443e-99   1st Qu.:4.011   1st Qu.: 4.694
    #>  Median :2.885e-99   Median :5.613   Median : 6.671
    #>  Mean   :2.885e-99   Mean   :5.613   Mean   : 6.671
    #>  3rd Qu.:4.328e-99   3rd Qu.:7.216   3rd Qu.: 8.647
    #>  Max.   :5.771e-99   Max.   :8.818   Max.   :10.623

    cat("\nAll bootstrap samples should converge successfully.\n")
    #>
    #> All bootstrap samples should converge successfully.
    cat("If many fail, consider:\n")
    #> If many fail, consider:
    cat("- Increasing max_iter in lm_pmm2()\n")
    #> - Increasing max_iter in lm_pmm2()
    cat("- Checking for outliers or influential points\n")
    #> - Checking for outliers or influential points
    cat("- Simplifying the model\n")
    #> - Simplifying the model

### Bootstrap Distribution Visualization

    # For demonstration, regenerate bootstrap samples
    set.seed(123)
    B <- 1000
    sample_size <- nrow(dat)
    boot_coefs <- matrix(0, nrow = B, ncol = 2)

    for(b in 1:B) {
      res_b <- sample(residuals(fit_pmm2), sample_size, replace = TRUE)
      y_b <- fitted(fit_pmm2) + res_b
      dat_b <- dat
      dat_b$y <- y_b

      fit_b <- lm_pmm2(y ~ x, data = dat_b, verbose = FALSE)
      boot_coefs[b, ] <- coef(fit_b)
    }

    # Scatter plot of bootstrap estimates
    par(mfrow = c(1, 2))

    # Bivariate distribution
    plot(boot_coefs[, 1], boot_coefs[, 2],
         pch = 16, col = rgb(0, 0, 1, 0.1),
         xlab = "Intercept", ylab = "Slope",
         main = "Joint Bootstrap Distribution")
    points(coef(fit_pmm2)[1], coef(fit_pmm2)[2],
           pch = 19, col = "red", cex = 2)

    # Correlation
    cor_boot <- cor(boot_coefs[, 1], boot_coefs[, 2])
    cat("Bootstrap correlation between intercept and slope:", round(cor_boot, 3), "\n")
    #> Bootstrap correlation between intercept and slope: -0.87

    # Density plot
    plot(density(boot_coefs[, 2]), main = "Slope Bootstrap Density",
         xlab = "Slope", lwd = 2, col = "blue")
    abline(v = beta_1, col = "red", lwd = 2, lty = 2)
    abline(v = coef(fit_pmm2)[2], col = "darkblue", lwd = 2)
    legend("topright", legend = c("True", "Estimate", "Bootstrap"),
           col = c("red", "darkblue", "blue"),
           lty = c(2, 1, 1), lwd = 2)

    par(mfrow = c(1, 1))

## Part 7: Practical Guidelines

### When to Use Bootstrap for PMM2

Recommended:

- Always for PMM2 inference—it’s the primary method
- When error distribution is clearly non-Gaussian (check Q-Q plots)
- For moderate to large samples (n ≥ 100)
- When you need robust confidence intervals

Alternative methods:

- Analytical standard errors exist but assume asymptotic normality
- May underestimate uncertainty with skewed errors

### Common Pitfalls and Solutions

Problem | Symptom | Solution
--- | --- | ---
Too few bootstrap samples | Wide variation in CIs across runs | Increase B to ≥ 1000
Non-convergence | Many failed bootstrap fits | Increase max_iter, check data quality
Extreme outliers | Very wide bootstrap CIs | Investigate outliers, consider robust methods
Small sample size | Unstable bootstrap distribution | Consider analytical methods, increase n if possible

## Part 8: Advanced Topics

### Studentized Bootstrap

For even better coverage, use studentized (pivotal) bootstrap:

    # Not yet implemented in EstemPMM
    # Future feature: studentized CIs for improved small-sample performance

### Block Bootstrap for Time Series

For time series with strong autocorrelation, block bootstrap may be needed:

    # Not yet implemented in EstemPMM
    # Current approach: residual bootstrap (adequate for most cases)
    # Future: moving block bootstrap for highly dependent series

## Summary

Bootstrap inference for PMM2 provides:

1. Distribution-free confidence intervals without normality assumptions
2. Robust standard errors accounting for error distribution skewness
3. Flexible hypothesis testing for linear and time series models
4. Practical diagnostics to assess estimation quality

### Best Practices Checklist

- Use B ≥ 1000 for standard inference
- Check convergence of bootstrap samples
- Compare PMM2 vs OLS standard errors to quantify efficiency
- Visualize bootstrap distributions to assess normality
- Use parallel computing for large B or complex models
- Report both percentile and bias-corrected CIs

## References

1. Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. CRC Press.

2. Davison, A. C., & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.

3. Zabolotnii, S., Warsza, Z.L., Tkachenko, O. (2018). “Polynomial Estimation of Linear Regression Parameters for the Asymmetric PDF of Errors”. Springer, AUTOMATION 2018.

4. Package functions: `?pmm2_inference`, `?ts_pmm2_inference`

5. GitHub: https://github.com/SZabolotnii/EstemPMM

## Next Steps

- Introduction to PMM2: See `vignette("01-pmm2-introduction")`
- Time Series Models: See `vignette("02-pmm2-time-series")`
- Package Demos: Run `demo(package = "EstemPMM")` for interactive examples